awesome_paper_date.md

Awesome papers by date

Here, we list some papers related to transfer learning by date (starting from 2021-07). For papers older than 2021-07, please refer to the papers by topic, which contains more papers.

• Awesome papers by date
  • 2024-04
  • 2024-03
  • 2024-02
  • 2024-01
  • 2023-12
  • 2023-11
  • 2023-10
  • 2023-09
  • 2023-08
  • 2023-07
  • 2023-06
  • 2023-05
  • 2023-04
  • 2023-03
  • 2023-02
  • 2023-01
  • 2022-12
  • 2022-11
  • 2022-10
  • 2022-09
  • 2022-08
  • 2022-07
  • 2022-06
  • 2022-05
  • 2022-04
  • 2022-03
  • 2022-02
  • 2022-01
  • 2021-12
  • 2021-11
  • 2021-10
  • 2021-09
  • 2021-08
  • 2021-07

2024-04

• MDDD: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition [arxiv]

  • Manifold-based domain adaptation for EEG-based emotion recognition 基于流形的DA用于EEG情绪识别

• Domain Adaptation for Learned Image Compression with Supervised Adapters [arxiv]

  • Domain adaptation for learned image compression DA用于图片压缩

• Test-Time Training on Graphs with Large Language Models (LLMs) [arxiv]

  • Test-time training on graphs with LLMs 使用大语言模型在图上进行测试时训练

• DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series [arxiv]

  • Domain adaptation for anomaly detection 使用域自适应进行时间序列异常检测

• CVPR'24 Exploring the Transferability of Visual Prompting for Multimodal Large Language Models [arxiv]

  • Explore the transferability of visual prompting for multimodal LLM 探索多模态大模型visual prompt tuning的可迁移性

• DGMamba: Domain Generalization via Generalized State Space Model [arXiv]

  • Domain generalization using mamba 用Mamba结构进行DG

• CVPR'24 Unified Language-driven Zero-shot Domain Adaptation [arxiv]

  • Language-driven zero-shot domain adaptation 语言驱动的零样本 DA

• ICASSP'24 Learning Inference-Time Drift Sensor-Actuator for Domain Generalization [IEEE]

  • Inference-time drift actuator for OOD generalization

• ICASSP'24 SBM: Smoothness-Based Minimization for Domain Generalization [IEEE]

  • Smoothness-based minimization for OOD generalization

• ICASSP'24 G2G: Generalized Learning by Cross-Domain Knowledge Transfer for Federated Domain Generalization [IEEE]

  • Federated domain generalization

• ICASSP'24 Single-Source Domain Generalization in Fundus Image Segmentation Via Moderating and Interpolating Input Space Augmentation [IEEE]

  • Single-source DG in fundus image segmentation

• ICASSP'24 Style Factorization: Explore Diverse Style Variation for Domain Generalization [IEEE]

  • Style variation for domain generalization

• ICASSP'24 SPDG-Net: Semantics Preserving Domain Augmentation through Style Interpolation for Multi-Source Domain Generalization [IEEE]

  • Domain augmentation for multi-source DG

• ICASSP'24 Domaindiff: Boost out-of-Distribution Generalization with Synthetic Data [IEEE]

  • Using synthetic data for OOD generalization

• ICASSP'24 Multi-Level Augmentation Consistency Learning and Sample Selection for Semi-Supervised Domain Generalization [IEEE]

  • Multi-level augmentation for semi-supervised domain generalization

• ICASSP'24 MMS: Morphology-Mixup Stylized Data Generation for Single Domain Generalization in Medical Image Segmentation [IEEE]

  • Morphology-mixup for domain generalization

2024-03

• On the Benefits of Over-parameterization for Out-of-Distribution Generalization [arxiv]

  • Over-parameterazation for OOD generalizaiton 分析了过参数化对OOD的影响

• CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning [arxiv]

  • Chain-of-domain adaptation with visual prompt tuning 领域链adaptation

• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [arxiv]

  • Domain adaptation for eye-tracking systems 用DA进行眼球追踪

• EAGLE: A Domain Generalization Framework for AI-generated Text Detection [arxiv]

  • Domain generalization for AI content detection 用DG进行AI生成内容检测

• DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization [arxiv]

  • Dynamic propmtstyler for source-free DG 动态prompt分格化用于source-free DG

• Neurocomputing'24 Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation [arxiv]

  • Unvertainty-aware source-free domain adaptation 基于不确定性伪标签的domain adaptation

• Efficient Domain Adaptation for Endoscopic Visual Odometry [arxiv]

  • Efficient domain adaptation for visual odometry 高效DA用于odometry

• Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability [arxiv]

  • Domain adaptation in ecology and hydrology 研究生态学和水文学中的DA

• ICLR'24 SF(DA)2: Source-free Domain Adaptation Through the Lens of Data Augmentation [arxiv]

  • Source-free DA by data augmentation 通过数据增强来进行source-free DA

• CVPR'24 Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias [arxiv]

  • Unviersal semi-supervised DA 通过公共类bias进行半监督DA

• Domain Adaptation Using Pseudo Labels for COVID-19 Detection [arxiv]

  • Domain adaptation for COVID-19 detection 用DA进行covid-19检查

• Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans [arxiv]

  • Covid-19 test using domain adaptation 使用集成和测试增强用于DA covid-19

• V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions [arxiv]

  • DG for multi-agent perception 领域泛化用于极端天气

• Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification [arxiv]

  • Bidirectional multi-step DG for REID 双向领域泛化用于REID

• MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks [arxiv]

  • Model merge for medical transfer learning 通过模型合并进行医学迁移学习

• SPA: A Graph Spectral Alignment Perspective for Domain Adaptation [NeurIPS 2023] [Pytorch]

  • Graph spectral alignment and neighbor-aware propagation for domain adaptation

• Addressing Source Scale Bias via Image Warping for Domain Adaptation [arxiv]

  • Address the source scale bias for domain adaptation 解决源域的scale bias

• ICLR'24 扩展版 Learning with Noisy Foundation Models [arxiv]

  • Fine-tune a noisy foundation model 基础模型有noisy的时候如何finetune

• Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation [arxiv]

  • Foundation models help domain adaptation 基础模型帮助领域自适应

• Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling [arxiv]

  • Parameter-efficient adaptation for spatiotemporal modeling

• ICASSP'24 Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning [arxiv]

  • Test-time distribution learning adapter

• A Study on Domain Generalization for Failure Detection through Human Reactions in HRI [arxiv]

  • Domain generalization for failure detection through human reactions in HRI

• ICLR'24 Towards Robust Out-of-Distribution Generalization Bounds via Sharpness [arxiv]

  • Robust OOD generalization bounds

• Learning with Noisy Foundation Models [arxiv]

  • Learning with noisy foundation models

2024-02

• Unsupervised Domain Adaptation within Deep Foundation Latent Spaces [arxiv]
  • Domain adaptation using foundation models

2024-01

• Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning? [arxiv]

  • A comparison between visual prompt tuning and full finetuning 比较prompt tuning和全finetune

• Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy [arxiv]

  • OOD detection for ablated learned temperature energy

• LanDA: Language-Guided Multi-Source Domain Adaptation [arxiv]

  • Language guided multi-source DA 在多源域自适应中使用语言指导

• AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space [arxiv]

  • Semi-spuervised domain adaptation in the embedding space 在嵌入空间中进行半监督域自适应

• Inter-Domain Mixup for Semi-Supervised Domain Adaptation [arxiv]

  • Inter-domain mixup for semi-supervised domain adaptation 跨领域mixup用于半监督域自适应

• Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation [arxiv]

  • Source-free and image-only unsupervised domain adaptation

• ICLR'24 spotlight Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [arxiv]

  • A new research direction of transfer learning in the era of foundation models 大模型时代一个新研究方向：研究预训练数据的噪声对下游任务影响

• ICLR'24 Supervised Knowledge Makes Large Language Models Better In-context Learners [arxiv]

  • Small models help large language models for better OOD 用小模型帮助大模型进行更好的OOD

• NeurIPS'23 Geodesic Multi-Modal Mixup for Robust Fine-Tuning [paper]

  • Geodesic mixup for robust fine-tuning

• NeurIPS'23 Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models [paper]

  • Parameter and computation efficient transfer learning by reinforcement learning

• NeurIPS'23 Test-Time Distribution Normalization for Contrastively Learned Visual-language Models [paper]

  • Test-time distribution normalization for contrastively learned VLM

• NeurIPS'23 A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP) [paper]

  • A fine-gained analysis of CLIP robustness

• NeurIPS'23 When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation [paper]

  • Source-free domain adaptation using visual prompt tuning

• NeurIPS'23 CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation [arxiv]

  • Open set domain generalization using extra classes

• CPAL'24 FIXED: Frustratingly Easy Domain Generalization with Mixup [arxiv]

  • Easy domain generalization with mixup

• SDM'24 Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution [arxiv]

  • Optimization and model selection for domain generalization

• Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation [arxiv]

  • SAM for single-source domain generalization

• Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition [arxiv]

  • Multi-source DA with Transformer-based feature generation

2023-12

• Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding [arxiv]

  • Multi-modal domain adaptation 多模态领域自适应

• Domain Adaptive Graph Classification [arxiv]

  • Domain adaptive graph classification 域适应的图分类

• Understanding and Estimating Domain Complexity Across Domains [arxiv]

  • Understanding and estimating domain complexity 解释领域复杂性

• Prompt-based Domain Discrimination for Multi-source Time Series Domain Adaptation [arxiv]

  • Prompt-based domain discrimination for time series domain adaptation 基于prompt的时间序列域自适应

• NeurIPS'23 SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models [arxiv]

  • Test-time prompt adaptation for vision language models 对视觉-语言大模型的测试时prompt自适应

• AAAI24 Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects [arxiv][code]

  • A training-free test-time adaptation approach to relax the instance-specific prompts requirment in SAM.

• Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features [arxiv]

  • Open domain generalization with a single network 用单一网络结构进行开放式domain generalizaition

• Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation [arxiv]

  • Using vision foundation models for domain genealized semantic segmentation 用视觉基础模型进行域泛化语义分割

• DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation [arxiv]

  • Dynamic adaptation for cross-domain few-shot segmentation 动态适配用于跨领域小样本分割

• A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting [arxiv]

  • Instance weighting for domain adaptation 样本加权用于领域自适应

• Target-agnostic Source-free Domain Adaptation for Regression Tasks [arxiv]

  • Target-agnostic source-free DA for regression 用于回归任务的source-free DA

• On the Out-Of-Distribution Robustness of Self-Supervised Representation Learning for Phonocardiogram Signals [arxiv]

  • OOD robustness for self-supervised learning for phonocardiogram 心音图信号自监督的OOD鲁棒性

• Student Activity Recognition in Classroom Environments using Transfer Learning [arxiv]

  • Using transfer learning to recognize student activities 用迁移学习来识别学生课堂行为

2023-11

• A2XP: Towards Private Domain Generalization [arxiv]

  • Private domain generalization 隐私保护的domain generalization

• Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation [arxiv]

  • Auto-weighting for test-time adaptation 自动权重的TTA

• Domain Generalization by Learning from Privileged Medical Imaging Information [arxiv]

  • Domain generalizaiton by learning from privileged medical imageing inforamtion

• SSL-DG: Rethinking and Fusing Semi-supervised Learning and Domain Generalization in Medical Image Segmentation [arxiv]

  • Semi-supervised learning + domain generalization 把半监督和领域泛化结合在一起

• WACV'24 Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization [arxiv]

  • Class and domain augmentation for single-source open-domain DG 结合类和domain增强做单源DG

• Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector [arxiv]

  • Proposal-level unsupervised domain adaptation

• Robust Fine-Tuning of Vision-Language Models for Domain Generalization [arxiv]

  • Robust fine-tuning for domain generalization 用于领域泛化的鲁棒微调

• NeurIPS 2023 Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models [arxiv]

  • Distill OOD robustness from vision-language foundational models 从VLM模型中蒸馏出OOD鲁棒性

• UbiComp 2024 Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition [arxiv]

  • Test-time adaptation for activity recognition 测试时adaptation用于行为识别

2023-10

• PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization [arxiv]

  • Prompt-driven style generation for source-free domain generalization

• A Survey of Heterogeneous Transfer Learning [arxiv]

  • A recent survey of heterogeneous transfer learning 一篇最近的关于异构迁移学习的综述

• Equivariant Adaptation of Large Pre-Trained Models [arxiv]

  • Equivariant adaptation of large pre-trained models 对大模型进行等边自适应

• Effective and Parameter-Efficient Reusing Fine-Tuned Models [arxiv]

  • Effective and parameter-efficient reusing fine-tuned models 高效使用预训练模型

• Prompting-based Efficient Temporal Domain Generalization [arxiv]

  • Prompt based temporal domain generalization 基于prompt的时间域domain generalization

• Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [arxiv]

  • Noisy model learning: fine-tuning to supress the bad effect of noisy pretraining data 通过使用轻量级finetune减少噪音预训练数据对下游任务的影响

• ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning [arxiv]

  • Black-box foundation models for personalized federated learning 黑盒的blackbox模型进行个性化迁移学习

2023-09

• Domain Generalization with Fourier Transform and Soft Thresholding [arxiv]

  • Domain generalization with Fourier transform 基于傅里叶变换和软阈值进行domain generalization

• DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning [arxiv]

  • Decomposed prompt tuning for parameter-efficient fine-tuning 基于分解prompt tuning的参数高效微调

• Better Practices for Domain Adaptation [arxiv]

  • Better practice for domain adaptation

• Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images [arxiv]

  • Domain adaptation for efficient ViT

• Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning [arxiv]

  • Activity recognition using domain adaptation

2023-08

• IJCV'23 Exploring Vision-Language Models for Imbalanced Learning [arxiv] [code]

  • Explore vision-language models for imbalanced learning 探索视觉大模型在不平衡问题上的表现

• ICCV'23 Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning [arxiv] [code]

  • 达到对抗鲁棒性和泛化能力的trade off

• ICCV'23 Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation [arxiv]

  • Domain-specificity for source-free DA 用领域特异性驱动的source-free DA

• Unsupervised Domain Adaptation via Domain-Adaptive Diffusion [arxiv]

  • Domain-adaptive diffusion for domain adaptation 领域自适应的diffusion

• Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization [arxiv]

  • Multi-scale and multi-layer contrastive learning for DG 多尺度和多层对比学习用于DG

• Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy [arxiv]

  • Domain generalization for diabetic retinopathy 用领域泛化进行糖尿病视网膜病

• Federated Fine-tuning of Billion-Sized Language Models across Mobile Devices [arxiv]

  • Federated fine-tuning for large models 大模型联邦微调

• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [arxiv]

  • Source-free domain adaptation for MRI analysis

• Towards Realistic Unsupervised Fine-tuning with CLIP [arxiv]

  • Unsupervised fine-tuning of CLIP

• Fine-tuning can cripple your foundation model; preserving features may be the solution [arxiv]

  • Fine-tuning will cripple foundation model

• Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models [arxiv]

  • Transfer learning for medical image segmentation

• Transfer Learning for Portfolio Optimization [arxiv]

  • Transfer learning for portfolio optimization

• NormAUG: Normalization-guided Augmentation for Domain Generalization [arxiv]

  • Normalization augmentation for domain generalization

2023-07

• Benchmarking Algorithms for Federated Domain Generalization [arxiv]

  • Benchmark algorthms for federated domain generalization 对联邦域泛化算法进行的benchmark

• DISPEL: Domain Generalization via Domain-Specific Liberating [arxiv]

  • Domain generalization via domain-specific liberating

• Review of Large Vision Models and Visual Prompt Engineering [arxiv]

  • A survey of large vision model and prompt tuning 一个关于大视觉模型的prompt tuning的综述

• Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization [arxiv]

  • Exemplar-based style synthesis for domain generalization 样例格式合成用于DG

• SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation [arxiv]

  • Using SAM for domain adaptation 使用segment anything进行domain adaptation

• Unified Transfer Learning Models for High-Dimensional Linear Regression [arxiv]

  • Transfer learning for high-dimensional linar regression 迁移学习用于高维线性回归

2023-06

• Pruning for Better Domain Generalizability [arxiv]

  • Using pruning for better domain generalization 使用剪枝操作进行domain generalization

• TMLR'23 Generalizability of Adversarial Robustness Under Distribution Shifts [openreview]

  • Evaluate the OOD perormance of adversarial training 评测对抗训练模型的OOD鲁棒性

• Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning [arxiv]

  • A guide for parameter-efficient fine-tuning 一个对parameter efficient fine-tuning的全面介绍

• ICML'23 A Kernel-Based View of Language Model Fine-Tuning [arxiv]

  • A kernel-based view of language model fine-tuning 一种以kernel的视角来看待fine-tuning的方法

• ICML'23 Improving Visual Prompt Tuning for Self-supervised Vision Transformers [arxiv]

  • Improving visual prompt tuning for self-supervision 为自监督模型提高其 prompt tuning 表现

• Cross-Database and Cross-Channel ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation [arxiv]

  • EEG using unsupervised domain adaptation 用无监督DA来进行EEG心跳分类

• Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification [arxiv]

  • Real-time online unsupervised domain adaptation for REID 无监督DA用于REID

• Federated Domain Generalization: A Survey [arxiv]

  • A survey on federated domain generalization 一篇关于联邦域泛化的综述

• Domain Generalization for Domain-Linked Classes [arxiv]

  • Domain generalization for domain-linked classes

• Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? A Metric for Unsupervised Evaluation of Domain Adaptation [arxiv]

  • Evaluate domain adaptation models 评测domain adaptation的模型

• Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction [arxiv]

  • Universal test-time adaptation

• Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting [arxiv]

  • Using dynamic visual prompting for model adaptation 用动态视觉prompt进行模型适配

2023-05

• Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup [arxiv]

  • Why mixup works for domain generalization? 系统性研究为啥mixup对OOD很work

• ACL'23 Parameter-Efficient Fine-Tuning without Introducing New Latency [arxiv]

  • Parameter-efficient finetuning 参数高效的finetune

• Universal Domain Adaptation from Foundation Models [arxiv]

  • Using foundation models for universal domain adaptation

• Ahead-of-Time P-Tuning [arxiv]

  • Ahead-ot-time P-tuning for language models

• Multi-Source to Multi-Target Decentralized Federated Domain Adaptation [arxiv]

  • Decentralized federated domain adaptation

• Benchmarking Low-Shot Robustness to Natural Distribution Shifts [arxiv]

  • Low-shot robustness to distribution shifts

2023-04

• Multi-Source to Multi-Target Decentralized Federated Domain Adaptation [arxiv]

  • Multi-source to multi-target federated domain adaptation 多源多目标的联邦域自适应

• ICML'23 AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation [arxiv]

  • Adaptive test-time adaptation 非参数化分类器进行测试时adaptation

• Improved Test-Time Adaptation for Domain Generalization [arxiv]

  • Improved test-time adaptation for domain generalization

• Reweighted Mixup for Subpopulation Shift [arxiv]

  • Reweighted mixup for subpopulation shift

• CVPR'23 Zero-shot Generative Model Adaptation via Image-specific Prompt Learning [arxiv]

  • Zero-shot generative model adaptation via image-specific prompt learning 零样本的生成模型adaptation

• Source-free Domain Adaptation Requires Penalized Diversity [arxiv]

  • Source-free DA requires penalized diversity

• Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation [arxiv]

  • Domain generalization for medical segmentation 用domain generalization进行医学分割

• CVPR'23 Meta-causal Learning for Single Domain Generalization [arxiv]

  • Meta-causal learning for domain generalization

• Domain Generalization In Robust Invariant Representation [arxiv]

  • Domain generalization in robust invariant representation

• Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness [arxiv]

  • Local structure preserving for adversarial robustness 通过保留局部结构来进行对抗鲁棒性

• TFS-ViT: Token-Level Feature Stylization for Domain Generalization [arxiv]

  • Token-level feature stylization for domain generalization 用token-level特征变换进行domain generalization

• Are Data-driven Explanations Robust against Out-of-distribution Data? [arxiv]

  • Data-driven explanations robust? 探索数据驱动的解释是否是OOD鲁棒的

• ERM++: An Improved Baseline for Domain Generalization [arxiv]

  • Improved ERM for domain generalization 提高的ERM用于domain generalization

• CVPR'23 Feature Alignment and Uniformity for Test Time Adaptation [arxiv]

  • Feature alignment for test-time adaptation 使用特征对齐进行测试时adaptation

• Finding Competence Regions in Domain Generalization [arxiv]

  • Finding competence regions in domain generalization 在DG中发现能力区域

• CVPR'23 TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [arxiv]

  • Improve generalization and adversarial robustness 同时提高鲁棒性和泛化性

• CVPR'23 Trainable Projected Gradient Method for Robust Fine-tuning [arxiv]

  • Trainable PGD for robust fine-tuning 可训练的pgd用于鲁棒的微调技术

• Parameter-Efficient Tuning Makes a Good Classification Head [arxiv]

  • Parameter-efficient tuning makes a good classification head 参数高效的迁移学习成就一个好的分类头

• Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]

  • Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习

2023-03

• CVPR'23 A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation [arxiv]

  • A new benchmark for domain adaptation 一个对于domain adaptation最新的benchmark

• Unsupervised domain adaptation by learning using privileged information [arxiv]

  • Domain adaptation by privileged information 使用高级信息进行domain adaptation

• A Unified Continual Learning Framework with General Parameter-Efficient Tuning [arxiv]

  • A continual learning framework for parameter-efficient tuning 一个对于参数高效迁移的连续学习框架

• CVPR'23 Sharpness-Aware Gradient Matching for Domain Generalization [arxiv]

  • Sharpness-aware gradient matching for DG 利用梯度匹配进行domain generalization

• TempT: Temporal consistency for Test-time adaptation [arxiv]

  • Temporeal consistency for test-time adaptation 时间一致性用于test-time adaptation

• TMLR'23 Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks [arxiv]

  • A framework for online learning 一个在线学习的框架

• ICLR'23 workshop SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models [arxiv]

  • Sparse pre-training and dense fine-tuning

• CVPR'23 ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization [arxiv]

  • A lightweight module for domain generalization 一个用于DG的轻量级模块

• ICLR'23 Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning [arxiv]

  • Contrastive alignment for vision language models using transfer learning 使用参数高效迁移进行视觉语言模型的对比对齐

• Probabilistic Domain Adaptation for Biomedical Image Segmentation [arxiv]

  • Probabilistic domain adaptation for biomedical image segmentation 概率的domain adaptation用于生物医疗图像分割

• Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology [arxiv]

  • Imbalanced domain generalization for single cell classification 不平衡的DG用于单细胞分类

• Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm [arxiv]

  • Parameter-efficient transfer learning: a two-stage approach 一种两阶段的参数高效迁移学习

• Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow [arxiv]

  • Domain adaptation for foggy scene optical flow 领域自适应用于雾场景的光流

• ICLR'23 AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks [arxiv]

  • GNN with autoML transfer learning 用于GNN的自动迁移学习

• Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy [arxiv]

  • Transfer learning for Depression detection 迁移学习用于脉动计焦虑检测

• Domain Generalization via Nuclear Norm Regularization [arxiv]

  • Domain generalization via nuclear norm regularization 使用核归一化进行domain generalization

• To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning [arxiv]

  • Ensembling in transfer learning 调研迁移学习中的集成

• CVPR'13 Masked Images Are Counterfactual Samples for Robust Fine-tuning [arxiv]

  • Masked images for robust fine-tuning 调研masked image对于fine-tuning的影响

• FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning [arxiv]

  • Fast generalization for federated CLIP 在联邦中进行快速的CLIP训练

• Robust Representation Learning with Self-Distillation for Domain Generalization [arxiv]

  • Robust representation learning with self-distillation

• ICLR-23 Temporal Coherent Test-Time Optimization for Robust Video Classification [arxiv]

  • Temporal distribution shift in video classification

• WSDM-23 A tutorial on domain generalization [link] | [website]

  • A tutorial on domain generalization

2023-02

• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective [arxiv] | [code]

  • Adversarial and OOD evaluation of ChatGPT 对ChatGPT鲁棒性的评测

• Transfer learning for process design with reinforcement learning [arxiv]

  • Transfer learning for process design with reinforcement learning 使用强化迁移学习进行过程设计

• Domain Adaptation for Time Series Under Feature and Label Shifts [arxiv]

  • Domain adaptation for time series 用于时间序列的domain adaptation

• How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? [arxiv]

  • Regression models uncertainty for distribution shift 回归模型对于分布漂移的不确定性

• ICLR'23 SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning [arxiv]

  • Semi-supervised learning algorithm 解决标签质量问题的半监督学习方法

• Empirical Study on Optimizer Selection for Out-of-Distribution Generalization [arxiv]

  • Opimizer selection for OOD generalization OOD泛化中的学习器选择

• ICML'22 Understanding the failure modes of out-of-distribution generalization [arxiv]

  • Understand the failure modes of OOD generalization 探索OOD泛化中的失败现象

• ICLR'23 Out-of-distribution Representation Learning for Time Series Classification [arxiv]

  • OOD for time series classification 时间序列分类的OOD算法

2023-01

• ICLR'23 FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning [arxiv]

  • New baseline for semi-supervised learning 半监督学习新算法

• CLIP the Gap: A Single Domain Generalization Approach for Object Detection [arxiv]

  • Using CLIP for domain generalization object detection 使用CLIP进行域泛化的目标检测

• Language-Informed Transfer Learning for Embodied Household Activities [arxiv]

  • Transfer learning for robust control in household 在家居机器人上使用强化迁移学习

• Does progress on ImageNet transfer to real-world datasets? [arxiv]

  • ImageNet accuracy does not transfer to down-stream tasks

• TPAMI'23 Source-Free Unsupervised Domain Adaptation: A Survey [arxiv]

  • A survey on source-free domain adaptation 关于source-free DA的一个最新综述

• Discriminative Radial Domain Adaptation [arxiv]

  • Discriminative radial domain adaptation 判别性的放射式domain adaptation

2022-12

• WACV'23 Cross-Domain Video Anomaly Detection without Target Domain Adaptation [arxiv]

  • Cross-domain video anomaly detection without target domain adaptation 跨域视频异常检测

• Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation [arxiv]

  • Pre-trained models for source-free domain adaptation 用预训练模型进行source-free DA

• TMLR'22 A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges [openreview]

  • A recent survey on OOD/anomaly detection 一篇最新的关于OOD/anomaly detection的综述

• NeurIPS'18 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [paper]

  • Using class-conditional distribution for OOD detection 使用类条件概率进行OOD检测

• ICLR'22 Discrete Representations Strengthen Vision Transformer Robustness [arxiv]

  • Embed discrete representation for OOD generalization 在ViT中加入离散表征增强OOD性能

• CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars [arxiv]

  • Continual DA for self-driving cars 连续的domain adaptation用于自动驾驶

• Finetune like you pretrain: Improved finetuning of zero-shot vision models [arxiv]]

  • Improved fine-tuning of zero-shot models 针对zero-shot model提高fine-tuneing

2022-11

• ECCV-22 DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [arXiv] [Code]

  • Domain adaptation in semantic segmentation 语义分割域适应

• Robust Mean Teacher for Continual and Gradual Test-Time Adaptation [arxiv]

  • Mean teacher for test-time adaptation 在测试时用mean teacher进行适配

• Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](Learning to Learn Domain-invariant Parameters for Domain Generalization)]

  • Learning to learn domain-invariant parameters for DG 元学习进行domain generalization

• HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization [arxiv]

  • Hypernetwork-based ensembling for domain generalization 超网络构成的集成学习用于domain generalization

• The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [arxiv]

  • OOD using fine-tuning 系统总结了基于fine-tuning进行OOD的一些结果

• GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective [arxiv]

  • OOD for natural language processing evaluation 提出GLUE-X用于OOD在NLP数据上的评估

• CVPR'22 Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts [arxiv]

  • Vision transformers generalization under distribution shifts 评估ViT的分布漂移

• NeurIPS'22 Models Out of Line: A Fourier Lens on Distribution Shift Robustness [arxiv]

  • A fourier lens on distribution shift robustness 通过傅里叶视角来看分布漂移的鲁棒性

• CVPR'22 Does Robustness on ImageNet Transfer to Downstream Tasks? [arxiv]

  • Does robustness on imagenet transfer lto downstream tasks?

• Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [arxiv]

  • Normalization perturbation for domain generalization 通过归一化扰动来进行domain generalization

• FIXED: Frustraitingly easy domain generalization using Mixup [arxiv]

  • 使用Mixup进行domain generalization

• Learning to Learn Domain-invariant Parameters for Domain Generalization [arxiv]

  • Learning to learn domain-invariant parameters for domain generalization

• NeurIPS'22 Improved Fine-Tuning by Better Leveraging Pre-Training Data [openreview]

  • Using pre-training data for fine-tuning 用预训练数据来做微调

• NeurIPS'22 Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [openreview]

  • Adaptive contrastive learning for source-free DA 自适应的对比学习用于source-free DA

• NeurIPS'22 LOG: Active Model Adaptation for Label-Efficient OOD Generalization [openreview]

  • Model adaptation for label-efficient OOD generalization

• NeurIPS'22 MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification [openreview]

  • Multi-model domain adaptation mor medical image classification 多模型DA用于医疗数据

• NeurIPS'22 Domain Adaptation under Open Set Label Shift [openreview]

  • Domain adaptation under open set label shift 在开放集的label shift中的DA

• NeurIPS'22 Domain Generalization without Excess Empirical Risk [openreview]

  • Domain generalization without excess empirical risk

• NeurIPS'22 FedSR: A Simple and Effective Domain Generalization Method for Federated Learning [openreview]

  • FedSR for federated learning domain generalization 用于联邦学习的domain generalization

• NeurIPS'22 Probable Domain Generalization via Quantile Risk Minimization [openreview]

  • Domain generalization with quantile risk minimization 用quantile风险最小化的domain generalization

• NeurIPS'22 Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer [arxiv]

  • Continual learning with backward knowledge transfer 反向知识迁移的持续学习

• NeurIPS'22 Test Time Adaptation via Conjugate Pseudo-labels [openreview]

  • Test-time adaptation with conjugate pseudo-labels 用伪标签进行测试时adaptation

• NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [openreview]

  • OOD models are not robust 分布外泛化模型不够鲁棒

2022-10

• NeurIPS'22 Respecting Transfer Gap in Knowledge Distillation [arxiv]

  • Transfer gap in distillation 知识蒸馏中的迁移gap

• Transfer of Machine Learning Fairness across Domains [arxiv]

  • Fairness transfer in transfer learning 迁移学习中的公平性迁移

• On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [arxiv]

  • Fine-tuned features for domain adaptation 微调的特征用于域自适应

• WACV-23 ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing [arxiv]

  • Domain adaptation for object detection using confidence mixing 用置信度mix做domain adaptation

• CVPR-20 Regularizing CNN Transfer Learning With Randomised Regression [arxiv]

  • Using randomized regression to regularize CNN 用随机回归约束CNN迁移学习

• AAAI-21 TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning [arxiv]

  • Pruning pre-trained model for transfer learning 通过对预训练模型进行剪枝来进行迁移学习

• PhDthesis Generalizing in the Real World with Representation Learning [arxiv]

  • A phd thesis about generalization in real world 一篇关于现实世界如何做Generalization的博士论文

• The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [arxiv]

  • Evolution of OOD robustness by fine-tuning

• Visual Prompt Tuning for Test-time Domain Adaptation [arxiv]

  • VPT for test-time adaptation 用prompt tuning进行test-time DA

• Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup [arxiv]

  • Domain adaptation for COVID-19 用DA进行COVID-19预测

• ICONIP'22 IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels [arxiv]

  • Intra-domain adaptation for segmentation 子领域对抗Adaptation

• NeurIPS'22 Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks [arxiv]

  • Parameter-efficient multi-task adaptation 参数高效的多任务adaptation

• Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning [arxiv]

  • OOD in algorithmic reasoning 算法reasoning过程中的OOD

• Towards Out-of-Distribution Adversarial Robustness [arxiv]

  • OOD adversarial robustness OOD对抗鲁棒性

• TripleE: Easy Domain Generalization via Episodic Replay [arxiv]

  • Easy domain generalization by episodic replay

• Deep Spatial Domain Generalization [arxiv]

  • Deep spatial domain generalization

2022-09

• Assaying Out-Of-Distribution Generalization in Transfer Learning [arXiv]

  • A lot of experiments to show OOD performance

• ICML-21 Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [arxiv]

  • Strong correlation between ID and OOD

• Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images [arxiv]

  • Bomb craters detection using domain adaptation 用DA检测遥感图像中的炮弹弹坑

• WACV-23 TeST: Test-time Self-Training under Distribution Shift [arxiv]

  • Test-time self-training 测试时训练

• StyleTime: Style Transfer for Synthetic Time Series Generation [arxiv]

  • Style transfer for time series generation 时间序列生成的风格迁移

• Robust Domain Adaptation for Machine Reading Comprehension [arxiv]

  • Domain adaptation for machine reading comprehension 机器阅读理解的domain adaptation

• Generalized representations learning for time series classification [arxiv]

  • OOD for time series classification 域泛化用于时间序列分类

• USB: A Unified Semi-supervised Learning Benchmark [arxiv] [code]

  • Unified semi-supervised learning codebase 半监督学习统一代码库

• Test-Time Training with Masked Autoencoders [arxiv]

  • Test-time training with MAE MAE的测试时训练

• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [arxiv]

  • Test-time prompt tuning 测试时的prompt tuning

• TeST: test-time self-training under distribution shift [arxiv]

  • Test-time self-training 测试时的self-training

• Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification [arxiv]

  • Domain generalization for cross-scene hyperspectral image classification 域泛化用于高光谱图像分类

• IEEE-TMM'22 Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments [IEEE]

  • Uncertainty modeling for domain adaptation 噪声环境下的domain adaptation

• Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation arxiv

  • OOD by frequency-based augmentation 通过基于频率的数据增强进行OOD

• Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study arxiv

  • Domain generalizationfor prostate segmentation 领域泛化用于前列腺分割

• Domain Adaptation from Scratch arxiv

  • Domain adaptation from scratch

• Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution arxiv

  • Model selection for domain generalization 域泛化中的模型选择问题

• Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

  • Parameter efficient CNN adapter for transfer learning 参数高效的CNN adapter用于迁移学习

• Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

  • Equivariant disentangled transformation for domain generalization 新的建模domain generalization的思路

2022-08

• ECCV-22 workshop Domain-Specific Risk Minimization

  • Domain-specific risk minization for OOD 领域特异性风险最小化用于域泛化

• TPAMI Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

  • Survey on semi and unsupervsed learning 半监督和无监督综述

• Improving video retrieval using multilingual knowledge transfer

  • Video retrieval using multilingual knowledge transfer 多语言知识迁移用于视频检索

• Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization

  • Battery health estimation using transfer learning 用迁移学习进行电池健康估计

• IJCAI-22 Domain Generalization through the Lens of Angular Invariance

  • Using angular invariance for domain generalization 使用角度不变性进行domain generalization

• MM-22 Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation

  • Transformer for domain adaptation 用transformer进行DA

• Adaptive Domain Generalization via Online Disagreement Minimization

  • Online domain generalization via disagreement minimization 在线DG

• Self-Distilled Vision Transformer for Domain Generalization

  • Vision transformer for domain generalization 用ViT做domain generalization

• NeurIPS-21 The balancing principle for parameter choice in distance-regularized domain adaptation

  • Hyperparameter selection for domain adaptation 对adaptation中的正则项系数进行选择

• Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder

  • Transfer learning for segmentation problems 统一表示迁移学习于分割问题的思路

2022-07

• TMLR-22 Domain-invariant Feature Exploration for Domain Generalization

  • Exploring domain-invariant feature for domain generalization 探索领域不变特征在领域泛化中的应用

• TIST-22 Domain Generalization for Activity Recognition via Adaptive Feature Fusion

  • Domain generalization for activity recognition 领域泛化用于行为识别

• ECCV-22 Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation

  • Prototype continual domain adaptation 基于原型的类增量domain adaptation

• Federated Semi-Supervised Domain Adaptation via Knowledge Transfer

  • Federated semi-supervised DA 联邦半监督DA

• Hyper-Representations for Pre-Training and Transfer Learning

  • Hyper-representation for pre-training and fine-tuning 对于预训练和微调的超表示

• MM-22 Source-Free Domain Adaptation for Real-world Image Dehazing

  • Source-free DA for image dehazing 无需源域的迁移用于图像去雾

• Improved OOD Generalization via Conditional Invariant Regularizer

  • Improved OOD generalization via conditional invariant regularizer 通过条件不变正则进行OOD泛化

• CVPR-22 Segmenting Across Places: The Need for Fair Transfer Learning With Satellite Imagery

  • Fair transfer learning with satellite imagery 公平迁移学习

• Transferability-Guided Cross-Domain Cross-Task Transfer Learning

  • Cross-domain cross-task transfer learning 用迁移性指标指导跨领域跨任务迁移

• Cross-Architecture Knowledge Distillation

  • Cross-architecture knowledge distillation 跨架构的知识蒸馏

• ECCV-22 Knowledge Condensation Distillation

  • Knowledge condensation distillation 知识压缩蒸馏

• An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications

  • Information-theoretic analysis for transfer learning 用信息理论解释迁移学习

• A Data-Based Perspective on Transfer Learning

  • Analyze the data numbers in transfer learning 分析迁移学习中数据的重要性

• PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

  • PAC-Bayesian domain adaptation 基于PAC-Bayesian的domain adaptation

2022-06

• NeurIPS-21 Parameterized Knowledge Transfer for Personalized Federated Learning

  • personalized group knowledge transfer training
  • 个性化群体知识迁移

• ICML-21 Federated Continual Learning with Weighted Inter-client Transfer

  • Federated Weighted Inter-client Transfer (FedWeIT) for Federated Continual Learning
  • 联邦加权客户端间传输方法，用于联邦持续学习

• SIGIR-21 FedCT: Federated Collaborative Transfer for Recommendation

  • Federated learning for cross-domain recommendation
  • 使用联邦迁移学习执行跨域推荐任务

• KDD-21 Federated Adversarial Debiasing for Fair and Transferable Representations

  • Federated Adversarial DEbiasing (FADE)
  • 通过对抗性学习对联邦学习过程去除偏见

• NeurIPS-20 Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge

  • Group knowledge transfer training
  • 群体知识迁移

• FL-IJCAI-22 MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare

  • MetaFed: a new form of federated learning 联邦之联邦学习、新范式

• Interspeech-22 Decoupled Federated Learning for ASR with Non-IID Data

  • Decoupled federated learning for non IID 解耦的联邦架构用于Non-IID语音识别

• Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

  • Few-shot DA for unsupervised constrastive learning 小样本DA用于无监督对比学习

• The Importance of Background Information for Out of Distribution Generalization

  • Background information for OOD generalization 背景信息对于OOD泛化的重要性

• Zero-Shot AutoML with Pretrained Models

  • 用预训练模型进行零样本的自动机器学习

• How robust are pre-trained models to distribution shift?

  • How robust are pre-trained models to distribution shift 评估预训练模型对于distribution shift的鲁棒性

• FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification

  • Few-shot transfer learning for image classification 小样本迁移学习用于图像分类

• COVID-19 Detection using Transfer Learning with Convolutional Neural Network

  • COVID-19 using transfer learning 用迁移学习进行COVID-19检测

• Wav2vec-S: Semi-Supervised Pre-Training for Speech Recognition

  • Pretraining for speech recognition 用预训练模型进行语音识别

• Causal Balancing for Domain Generalization

  • Causal balancing for domain generalization 因果平衡用于领域泛化

• NAACL-22 Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning

  • Transfer learning for zero-shot reasoning 迁移学习用于零次常识推理

• ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation

  • Fewshot UDA for medical image segmentation 小样本域自适应用于医疗图像分割

• One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift

  • Open set recognition with domain shift 开放集+domain shift

• Toward Certified Robustness Against Real-World Distribution Shifts

  • Certified robustness against real-world distribution shifts 真实世界中的distribution shift

• On Transfer Learning in Functional Linear Regression

  • Transfer learning in functional linear regression 迁移学习用于函数式线性回归

2022-05

• IJCAI-22 Parameter-Efficient Sparsity for Large Language Models Fine-Tuning

  • Parameter-efficient sparsity for language model fine-tuning 参数高效的稀疏学习用于语言模型微调

• A Domain-adaptive Pre-training Approach for Language Bias Detection in News

  • Domain-adaptive pre-training for language bias detection 领域适配预训练用于新闻语言偏见检测

• ScholarBERT: Bigger is Not Always Better

  • Empirical study on fine-tuning experiments 提出ScholarBERT进行大规模finetuning实验

• ICPR-22 OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation

  • Optimal transport-based domain adaptation 利用最优传输进行领域自适应

• Temporal Domain Generalization with Drift-Aware Dynamic Neural Network

  • Temporal domain generalization with drift-aware dynamic neural network 时序域泛化

• Active Source Free Domain Adaptation

  • Active source-free DA 主动学习-无源域DA

• Test-Time Robust Personalization for Federated Learning

  • Test-time robust personalization for FL 测试时鲁棒联邦学习

• FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

  • Self-adaptive thresholding for semi-supervised learning 新的自适应阈值半监督方法

• IJCAI-22 Test-time Fourier Style Calibration for Domain Generalization

  • Test-time calibration for domain generalization 用傅立叶变化进行域泛化的测试时矫正

• Multiple Domain Causal Networks

  • Mlutiple domain causal networks 多领域的因果网络

• ICLR-22 Enhancing Cross-lingual Transfer by Manifold Mixup

  • Cross-lingual transfer using manifold mixup 用Mixup进行cross-lingual transfer

• CVPR-22 workshop Online Unsupervised Domain Adaptation for Person Re-identification

  • Online domain adaptation for REID 在线adaptation

• Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

  • Time series domain adaptation 时间序列domain adaptation

• TIP-22 Spot-adaptive Knowledge Distillation

  • Spot-adaptive knowledge distillation 层次自适应的知识蒸馏

• NAACL-22 Efficient Few-Shot Fine-Tuning for Opinion Summarization

  • Few-shot fine-tuning for opinion summarization 小样本微调技术用于评论总结

• ICME-22 Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data

  • Unsupervised domain adaptation for infant pose recognition 用领域自适应进行婴儿姿势识别

2022-04

Updated at 2022-04-29:

• ACL-22 Probing Simile Knowledge from Pre-trained Language Models

  • Probe simile knowledge from pre-trained model 从预训练模型中找出明喻知识

• Parkinson's disease diagnostics using AI and natural language knowledge transfer

  • Transfer learning for Parkinson's disease diagnostics 迁移学习用于帕金森诊断

• CVPR-22 MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation

  • Multi-modal test-time adaptation for 3D semantic segmentation 多模态测试时adaptation用于3D语义分割

• Transfer Learning with Pre-trained Conditional Generative Models

  • Transfer learning with pre-trained conditional generative models 条件生成模型用于迁移学习

• ICLR-22 Towards a Unified View of Parameter-Efficient Transfer Learning

  • Unified view of parameter-efficient transfer learning 一个统一视角看待参数高效的迁移学习

• ICLR-22 Exploring the Limits of Large Scale Pre-training

  • Many experiments to explore pre-training 许多实验来探索预训练

• IEEE TNNLS-22 Towards Personalized Federated Learning

  • A survey on personalized federated learning 一个关于个性化联邦学习的综述

• On Effectively Learning of Knowledge in Continual Pre-training

  • Continual per-training 持续的预训练

• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models

  • Differential privacy by just fine-tune twice 通过微调两次进行差分隐私

• CVPR-22 Safe Self-Refinement for Transformer-based Domain Adaptation

  • Transformer-based domain adaptation 基于transformer的domain adaptation

• Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation

  • Handle the label shift in cross-domain semantic segmentation 在跨域语义分割时考虑label shift

• CVPR-22 workshop Out-Of-Distribution Detection In Unsupervised Continual Learning

  • OOD detection in unsupervised continual learning 无监督持续学习中进行OOD检测

• Transfer Learning for Autonomous Chatter Detection in Machining

  • Transfer learning for autonomous chatter detection

• NAACL-22 GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

  • Fast fine-tuning for content-based collaborative filtering
  • 快速的适用于协同过滤的微调

• AAAI-22 Powering Finetuning in Few-shot Learning: Domain-Agnostic Feature Adaptation with Rectified Class Prototypes

  • Finetuning in few-shot learning
  • 小样本学习中的微调

• CVPR-22 Does Robustness on ImageNet Transfer to Downstream Tasks?

  • Transfer learning robustness
  • 迁移学习鲁棒性

• Blockchain as an Enabler for Transfer Learning in Smart Environments

  • Blockchain transfer learning
  • 用区块链进行迁移学习

• ICLR-22 Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

  • Fin-tuning and linear probing for ood generalization
  • 先linear probing最后一层再finetune对OOD任务最好

• ICLR-22 Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks

  • Asymmetry learning for OOD tasks
  • 非对称学习用于OOD任务

2022-03

• Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detection

  • Channel masking for domain generalization object detection
  • 通过一个gate控制channel masking进行object detection DG

• A Broad Study of Pre-training for Domain Generalization and Adaptation

  • A broad study of pre-training models for DA and DG
  • 大量的实验进行DA和DG

• ISPASS-22 Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices

  • Benchmarking test-time adaptation for edge devices
  • 在端设备上评测test-time adaptation算法

• Multi-Source Domain Adaptation Based on Federated Knowledge Alignment

  • Multi-source domain adaptation
  • 多源域自适应

• Improving Generalization in Federated Learning by Seeking Flat Minima

  • Seeking flat minima for domain generalization in federated learning
  • 通过寻找平坦值进行联邦学习领域泛化

• CVPR-22 Decoupled Knowledge Distillation

  • Decoupled knowledge distillation
  • 解耦的知识蒸馏

• SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

  • Personalized federated learning
  • 个性化联邦学习

• ICSE-22 ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing | Code | Blog | Video

  • Safe transfer learning by reducing defect inheritance
  • 安全迁移学习的最新工作

• ACL-22 Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features

  • Language-agnostic meta-learning for TTS
  • 语言无关的元学习用于TTS

• Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models

  • Adapt unfamiliar inputs to frozen pretrained models
  • 让固定的预训练模型适配不熟悉的输入

• One Model, Multiple Tasks: Pathways for Natural Language Understanding

  • Pathways for natural language understanding
  • 使用一个model用于所有NLP任务

• Pre-trained Token-replaced Detection Model as Few-shot Learner

  • Pre-trained token-replaced detection model as few-shot learner
  • 预训练的替换token的检测模型

• Open Set Domain Adaptation By Novel Class Discovery

  • Open set DA by novel class discovery
  • 基于新类发现的open set da

• ICML-21 workshop Domain Adaptation with Factorizable Joint Shift

  • Domain adaptation with factorizable joint shift
  • 基于可分解的联合漂移的领域自适应

• ICC-22 Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization

  • Knowledge transfer in RL
  • 强化迁移学习

• ACL-22 Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings

  • Investigate selective prediction approaches in IID, OOD, and ADV settings
  • 在独立同分布、分布外、对抗情境中调研选择性预测方法

• PAKDD-22 Layer Adaptive Deep Neural Networks for Out-of-distribution Detection

  • Layer adaptive network for OOD detection
  • 层自适应的网络进行OOD检测

• Learning Semantic Segmentation from Multiple Datasets with Label Shifts

  • Learning semantic segmentation from many datasets with label shifts
  • 在有标签漂移的情况下从多个数据集中学习语义分割

• Causal Domain Adaptation with Copula Entropy based Conditional Independence Test

  • Use copula entropy based conditional independence test for csusal domain adaptation
  • 使用基于copula entopy的条件独立测试进行causal domain adaptation

• Interpretable Concept-based Prototypical Networks for Few-Shot Learning

  • Concept-based prototypical network for few-shot learning
  • 基于概念的原型网络用于小样本学习

2022-02

• How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?

  • Self-supervised learning for cross-domain few-shot
  • 自监督用于跨领域小样本

• Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations

  • Deep transfer learning for air quality estimate
  • 深度迁移学习用于卫星图到空气质量预测

• ICLR-22 oral A Fine-Grained Analysis on Distribution Shift

  • Extensive experiments on distribution shift for OOD
  • 大量的实验进行OOD验证

• ICLR-22 oral Fine-Tuning Distorts Pretrained Features and Underperforms Out-of-Distribution

  • Fine-tuning with linear probing for OOD
  • 微调加上linear probing用于OOD

• ICLR-22 spotlight Towards a Unified View of Parameter-Efficient Transfer Learning

  • Detailed analysis of parameter-efficient transfer learning
  • 对参数高效的迁移学习进行分析

• ICLR-22 Graph-Relational Domain Adaptation

  • Graph-relational domain adapttion using topological structures
  • 图级别的domain adaptation，使用拓扑结构

• Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

  • Transfer learning for traffic forecasting across cities
  • 用迁移学习进行跨城市的交通流量预测

• ICLR-22 Uncertainty Modeling for Out-of-Distribution Generalization

  • Uncertainty modeling for OOD generalization
  • 用于分布外泛化的不确定性建模

• ICLR-22 BEiT: BERT Pre-Training of Image Transformers

  • BERT pre-training of image transformers
  • 用BERT的方式pre-train transformer

• Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice

  • Using pre-training data to improve fine-tuning
  • 使用预训练数据来帮助finetune

2022-01

• IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

  • A benchmark for transfer learning in NLP
  • 一个用于NLP跨模态、任务、语言的benchmark

• Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study

  • Domain generalization in mass detection in mammography
  • Domain generalization进行胸部射线检测

• Domain-Invariant Representation Learning from EEG with Private Encoders

  • Domain-invariant learning from EEG
  • 用于EEG信号的领域不变特征研究

• Gap Minimization for Knowledge Sharing and Transfer

  • Multitask learning with gap minimization
  • 用于多任务学习的gap minimization方法

• DROPO: Sim-to-Real Transfer with Offline Domain Randomization

  • Sim-to-real transfer with domain randomization
  • 用domain randomization进行sim-to-real transfer

• AAAI-22 Knowledge Sharing via Domain Adaptation in Customs Fraud Detection

  • Domain adaptation for fraud detection
  • 用领域自适应进行欺诈检测

• Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation

  • Domain adaptation in semantic segmentation
  • 领域自适应在语义分割的应用

• KBS-22 Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features

  • An overview of transfer learning for time series data
  • 一个用迁移学习进行时间序列分析的小综述

• A Likelihood Ratio based Domain Adaptation Method for E2E Models

  • Domain adaptation for speech recognition
  • 用domain adaptation进行语音识别

• Transfer Learning for Scene Text Recognition in Indian Languages

  • Transfer learning for scene text recognition in Indian languages
  • 用迁移学习进行印度语的场景文字识别

• IEEE TMM-22 Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

  • Invariant and shared components for Faster RCNN detection
  • 解耦公共和私有表征进行目标检测

• Mixture of basis for interpretable continual learning with distribution shifts

  • Incremental learning with mixture of basis
  • 用mixture of domains进行增量学习

• TKDE-22 Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection

  • Adaptiev memory network for anomaly detection
  • 自适应的记忆网络用于异常检测

• ICIP-22 Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation

  • Meta-learning for domain generalization
  • 元学习用于domain generalization

• ICIP-22 Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains

  • Few-shot generalization using meta-learning
  • 用元学习进行小样本的泛化

• Data-Free Knowledge Transfer: A Survey

  • A survey on data-free distillation and source-free DA
  • 一篇关于data-free蒸馏和source-free DA的综述

• An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

  • An ensemble of pre-trained transformer for malware classification
  • 预训练的transformer通过集成进行恶意软件检测

• Optimal Representations for Covariate Shift

  • Learning optimal representations for covariate shift
  • 为covariate shift学习最优的表达

• Transfer-learning-based Surrogate Model for Thermal Conductivity of Nanofluids

  • Transfer learning for thermal conductivity
  • 迁移学习用于热传导

• Transfer learning of phase transitions in percolation and directed percolation

  • Transfer learning of phase transitions in percolation and directed percolation
  • 迁移学习用于precolation

• Transfer learning for cancer diagnosis in histopathological images

  • Transfer learning for cancer diagnosis
  • 迁移学习用于癌症诊断

2021-12

• IEEE TASLP-22 Exploiting Adapters for Cross-lingual Low-resource Speech Recognition Zhihu article

  • Cross-lingual speech recogntion using meta-learning and transfer learning
  • 用元学习和迁移学习进行跨语言的低资源语音识别

• More is Better: A Novel Multi-view Framework for Domain Generalization

  • Multi-view learning for domain generalization
  • 使用多视图学习来进行domain generalization

• SLIP: Self-supervision meets Language-Image Pre-training

  • Self-supervised learning + language image pretraining
  • 用自监督学习用于语言到图像的预训练

• Domain Prompts: Towards memory and compute efficient domain adaptation of ASR systems

  • Prompt for domain adaptation in speech recognition
  • 用Prompt在语音识别中进行domain adaptation

• UMAD: Universal Model Adaptation under Domain and Category Shift

  • Model adaptation under domain and category shift
  • 在domain和class都有shift的前提下进行模型适配

• Domain Adaptation on Point Clouds via Geometry-Aware Implicits

  • Domain adaptation for point cloud
  • 针对点云的domain adaptation

• A Survey of Unsupervised Domain Adaptation for Visual Recognition

  • A new survey article of domain adaptation
  • 对UDA的一个综述文章，来自作者博士论文

• VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

  • Vision-language efficient transfer learning
  • 参数高校的vision-language任务迁移

• Federated Learning with Adaptive Batchnorm for Personalized Healthcare

  • Federated learning with adaptive batchnorm
  • 用自适应BN进行个性化联邦学习

• Unsupervised Domain Adaptation: A Reality Check

  • Doing experiments to show the progress of DA methods over the years
  • 用大量的实验来验证近几年来DA方法的进展

• Hierarchical Optimal Transport for Unsupervised Domain Adaptation

  • hierarchical optimal transport for UDA
  • 层次性的最优传输用于domain adaptation

• Unsupervised Domain Generalization by Learning a Bridge Across Domains

  • Unsupervised domain generalization
  • 无监督的domain generalization

• Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning

  • Using soft pseudo-label and curriculum learning to boost UDA
  • 用软的伪标签和课程学习增强UDA方法

• Subtask-dominated Transfer Learning for Long-tail Person Search

  • Subtask-dominated transfer for long-tail person search
  • 子任务驱动的长尾人物搜索

• Revisiting the Transferability of Supervised Pretraining: an MLP Perspective

  • Revisit the transferability of supervised pretraining
  • 重新思考有监督预训练的可迁移性

• Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems

  • Multi-agent transfer in RL
  • 在RL中的多智能体迁移

• NeurIPS-21 On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

  • Theory and algorithm of domain-invariant learning for transfer learning
  • 对invariant representation的理论和算法

• WACV-22 Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation

  • Sample-level self-distillation for semi-supervised DA
  • 样本层次的自蒸馏用于半监督DA

• ROBIN : A Benchmark for Robustness to Individual Nuisancesin Real-World Out-of-Distribution Shifts

  • A benchmark for robustness to individual OOD
  • 一个OOD的benchmark

• ICML-21 workshop Towards Principled Disentanglement for Domain Generalization

  • Principled disentanglement for domain generalization
  • Principled解耦用于domain generalization

2021-11

• NeurIPS-21 workshop CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning

  • A large-scale dataset for bioimage transfer learning
  • 一个大规模的生物图像数据集用于迁移学习

• NeurIPS-21 workshop Component Transfer Learning for Deep RL Based on Abstract Representations

  • Deep transfer learning for RL
  • 深度迁移学习用于强化学习

• NeurIPS-21 workshop Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift

  • MMD for covariate shift
  • 用MMD来解决covariate shift问题

• Combined Scaling for Zero-shot Transfer Learning

  • Scaling up for zero-shot transfer learning
  • 增大训练规模用于zero-shot迁移学习

• Federated Learning with Domain Generalization

  • Federated domain generalization
  • 联邦学习+domain generalization

• Semi-Supervised Domain Generalization in Real World:New Benchmark and Strong Baseline

  • Semi-supervised domain generalization
  • 半监督+domain generalization

• MICCAI-21 Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

  • Domain generalization for mammography detection
  • 领域泛化用于乳房X射线检查

• On Representation Knowledge Distillation for Graph Neural Networks

  • Knowledge distillation for GNN
  • 适用于GNN的知识蒸馏

• BMVC-21 Domain Attention Consistency for Multi-Source Domain Adaptation

  • Multi-source domain adaptation using attention consistency
  • 用attention一致性进行多源的domain adaptation

• Action Recognition using Transfer Learning and Majority Voting for CSGO

  • Using transfer learning and majority voting for action recognition
  • 使用迁移学习和多数投票进行动作识别

• Open-Set Crowdsourcing using Multiple-Source Transfer Learning

  • Open-set crowdsourcing using multiple-source transfer learning
  • 使用多源迁移进行开放集的crowdsourcing

• Improved Regularization and Robustness for Fine-tuning in Neural Networks

  • Improve regularization and robustness for finetuning
  • 针对finetune提高其正则和鲁棒性

• TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

  • Temporal domain adaptation

• NeurIPS-21 Modular Gaussian Processes for Transfer Learning

  • Modular Gaussian process for transfer learning
  • 在迁移学习中使用modular Gaussian过程

• Estimating and Maximizing Mutual Information for Knowledge Distillation

  • Global and local mutual information maximation for knowledge distillation
  • 局部和全局互信息最大化用于蒸馏

• On Label Shift in Domain Adaptation via Wasserstein Distance

  • Using Wasserstein distance to solve label shift in domain adaptation
  • 在DA领域中用Wasserstein distance去解决label shift问题

• Xi-Learning: Successor Feature Transfer Learning for General Reward Functions

  • General reward function transfer learning in RL
  • 在强化学习中general reward function的迁移学习

• C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation

  • Cross-modality domain adaptation for medical image segmentation
  • 跨模态的DA用于医学图像分割

• Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation

  • Domain adaptation for multi-source entiry linkage
  • 用DA进行多源的实体链接

• Temporal Knowledge Distillation for On-device Audio Classification

  • Temporal knowledge distillation for on-device ASR
  • 时序知识蒸馏用于设备端的语音识别

• Transferring Domain-Agnostic Knowledge in Video Question Answering

  • Domain-agnostic learning for VQA
  • 在VQA任务中进行迁移学习

2021-10

• BMVC-21 SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition

  • Lighting transfer using implicit image decomposition
  • 用隐式图像分解进行光照迁移

• Domain Adaptation in Multi-View Embedding for Cross-Modal Video Retrieval

  • Domain adaptation for cross-modal video retrieval
  • 用领域自适应进行跨模态的视频检索

• Age and Gender Prediction using Deep CNNs and Transfer Learning

  • Age and gender prediction using transfer learning
  • 用迁移学习进行年龄和性别预测

• Domain Adaptation for Rare Classes Augmented with Synthetic Samples

  • Domain adaptation for rare class
  • 稀疏类的domain adaptation

• WACV-22 AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation

  • Test-time adaptation for video semantic segmentation
  • 测试时adaptation用于视频语义分割

• NeurIPS-21 Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning

  • Domain adaptation in reinforcement learning
  • 在强化学习中应用domain adaptation

• WACV-21 Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition

  • Domain generalization by audio-visual alignment
  • 通过音频-视频对齐进行domain generalization

• BMVC-21 Dynamic Feature Alignment for Semi-supervised Domain Adaptation

  • Dynamic feature alignment for semi-supervised DA
  • 动态特征对齐用于半监督DA

• NeurIPS-21 FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling 知乎解读 code

  • Curriculum pseudo label with a unified codebase TorchSSL
  • 半监督方法FlexMatch和统一算法库TorchSSL

• Rethinking supervised pre-training for better downstream transferring

  • Rethink better finetune
  • 重新思考预训练以便更好finetune

• Music Sentiment Transfer

  • Music sentiment transfer learning
  • 迁移学习用于音乐sentiment

• NeurIPS-21 Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

  • Source-free domain adaptation using constrastive learning
  • 无源域数据的DA，利用对比学习

• Understanding Domain Randomization for Sim-to-real Transfer

  • Understanding domain randomizationfor sim-to-real transfer
  • 对强化学习中的sim-to-real transfer进行理论上的分析

• Dynamically Decoding Source Domain Knowledge For Unseen Domain Generalization

  • Ensemble learning for domain generalization
  • 用集成学习进行domain generalization

• Scale Invariant Domain Generalization Image Recapture Detection

  • Scale invariant domain generalizaiton
  • 尺度不变的domain generalization

2021-09

• IEEE TIP-21 Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation

  • Clustering and discriminative alignment for DA
  • 聚类与判定式对齐用于DA

• IEEE TNNLS-21 Entropy Minimization Versus Diversity Maximization for Domain Adaptation

  • Entropy minimization versus diversity max for DA
  • 熵最小化与diversity最大化

• Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation

  • Adversarial domain adaptation for bronchoscopic depth estimation
  • 用对抗领域自适应进行支气管镜的深度估计

• EMNLP-21 Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

  • Few-shot intent detection using pretrain and finetune
  • 用迁移学习进行少样本意图检测

• EMNLP-21 Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

  • UDA for machine translation
  • 用领域自适应进行机器翻译

• KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation

  • Using Kronecker decomposition and knowledge distillation for pre-trained language models compression
  • 用Kronecker分解和知识蒸馏来进行语言模型的压缩

• Cross-Region Domain Adaptation for Class-level Alignment

  • Cross-region domain adaptation for class-level alignment
  • 跨区域的领域自适应用于类级别的对齐

• Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

  • Domain adaptation for cross-modality liver segmentation
  • 使用domain adaptation进行肝脏的跨模态分割

• CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

  • Cross-domain transformer for domain adaptation
  • 基于transformer进行domain adaptation

• ICCV-21 Shape-Biased Domain Generalization via Shock Graph Embeddings

  • Domain generalization based on shape information
  • 基于形状进行domain generalization

• Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation

  • Domain generalization for medical image segmentation
  • 领域泛化用于医学图像分割

• Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization

  • Domain generalization with wasserstein DRO
  • 使用Wasserstein DRO进行domain generalization

• FedZKT: Zero-Shot Knowledge Transfer towards Heterogeneous On-Device Models in Federated Learning

  • Zero-shot transfer in heterogeneous federated learning
  • 零次迁移用于联邦学习

• Fishr: Invariant Gradient Variances for Out-of-distribution Generalization

  • Invariant gradient variances for OOD generalization
  • 不变梯度方差，用于OOD

• How Does Adversarial Fine-Tuning Benefit BERT?

  • Examine how does adversarial fine-tuning help BERT
  • 探索对抗性finetune如何帮助BERT

• Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

  • Contrastive domain adaptation for QA
  • QA任务中应用对比domain adaptation

2021-08

• Robust Ensembling Network for Unsupervised Domain Adaptation

  • Ensembling network for domain adaptation
  • 集成嵌入网络用于domain adaptation

• Federated Multi-Task Learning under a Mixture of Distributions

  • Federated multi-task learning
  • 联邦多任务学习

• Fine-tuning is Fine in Federated Learning

  • Finetuning in federated learning
  • 在联邦学习中进行finetune

• Federated Multi-Target Domain Adaptation

  • Federated multi-target DA
  • 联邦学习场景下的多目标DA

• Learning Transferable Parameters for Unsupervised Domain Adaptation

  • Learning partial transfer parameters for DA
  • 学习适用于迁移部分的参数做UDA任务

• MICCAI-21 A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis

  • A benchmark of transfer learning for medical image
  • 一个详细的迁移学习用于医学图像的benchmark

• TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

  • Vision transformer for domain adaptation
  • 用视觉transformer进行DA

• CIKM-21 AdaRNN: Adaptive Learning and Forecasting of Time Series Code 知乎文章 Video

  • A new perspective to using transfer learning for time series analysis
  • 一种新的建模时间序列的迁移学习视角

• TKDE-21 Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

  • Anomaly detection using semi-supervised and transfer learning
  • 半监督学习用于无监督异常检测

• SemDIAL-21 Generating Personalized Dialogue via Multi-Task Meta-Learning

  • Generate personalized dialogue using multi-task meta-learning
  • 用多任务元学习生成个性化的对话

• ICCV-21 BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation

  • Bijective MMD for domain adaptation
  • 双射MMD用于语义分割

• A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

  • A survey on cross-domain recommendation
  • 跨领域的推荐的综述

• A Data Augmented Approach to Transfer Learning for Covid-19 Detection

  • Data augmentation to transfer learning for COVID
  • 迁移学习使用数据增强，用于COVID-19

• MM-21 Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding

  • Few-shot DA with image-to-class sparse similarity encoding
  • 小样本的领域自适应

• Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning

  • Prototype transfer and structure regularization
  • 原型的迁移学习

• Finetuning Pretrained Transformers into Variational Autoencoders

  • Finetune transformer to VAE
  • 把transformer迁移到VAE

• Pre-trained Models for Sonar Images

  • Pre-trained models for sonar images
  • 针对声纳图像的预训练模型

• Domain Adaptor Networks for Hyperspectral Image Recognition

  • Finetune for hyperspectral image recognition
  • 针对高光谱图像识别的迁移学习

2021-07

• CVPR-21 Efficient Conditional GAN Transfer With Knowledge Propagation Across Classes

  • Transfer conditional GANs to unseen classes
  • 通过知识传递，迁移预训练的conditional GAN到新类别

• CVPR-21 Ego-Exo: Transferring Visual Representations From Third-Person to First-Person Videos

  • Transfer learning from third-person to first-person video
  • 从第三人称视频迁移到第一人称

• Toward Co-creative Dungeon Generation via Transfer Learning

  • Game scene generation with transfer learning
  • 用迁移学习生成游戏场景

• Transfer Learning in Electronic Health Records through Clinical Concept Embedding

  • Transfer learning in electronic health record
  • 迁移学习用于医疗记录管理

• CVPR-21 Conditional Bures Metric for Domain Adaptation

  • A new metric for domain adaptation
  • 提出一个新的metric用于domain adaptation

• CVPR-21 Wasserstein Barycenter for Multi-Source Domain Adaptation

  • Use Wasserstein Barycenter for multi-source domain adaptation
  • 利用Wasserstein Barycenter进行DA

• CVPR-21 Generalized Domain Adaptation

  • A general definition for domain adaptation
  • 一个更抽象更一般的domain adaptation定义

• CVPR-21 Reducing Domain Gap by Reducing Style Bias

  • Syle-invariant training for adaptation and generalization
  • 通过训练图像对style无法辨别来进行DA和DG

• CVPR-21 Uncertainty-Guided Model Generalization to Unseen Domains

  • Uncertainty-guided generalization
  • 基于不确定性的domain generalization

• CVPR-21 Adaptive Methods for Real-World Domain Generalization

  • Adaptive methods for domain generalization
  • 动态算法，用于domain generalization

• 20210716 ICML-21 Continual Learning in the Teacher-Student Setup: Impact of Task Similarity

  • Investigating task similarity in teacher-student learning
  • 调研在continual learning下teacher-student learning问题的任务相似度

• 20210716 BMCV-extend Exploring Dropout Discriminator for Domain Adaptation

  • Using multiple discriminators for domain adaptation
  • 用分布估计代替点估计来做domain adaptation

• 20210716 TPAMI-21 Lifelong Teacher-Student Network Learning

  • Lifelong distillation
  • 持续的知识蒸馏

• 20210716 MICCAI-21 Few-Shot Domain Adaptation with Polymorphic Transformers

  • Few-shot domain adaptation with polymorphic transformer
  • 用多模态transformer做少样本的domain adaptation

• 20210716 InterSpeech-21 Speech2Video: Cross-Modal Distillation for Speech to Video Generation

  • Cross-model distillation for video generation
  • 跨模态蒸馏用于语音到video的生成

• 20210716 ICML-21 workshop Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning

  • Using domain adaptation for geospatial ML
  • 用domain adaptation进行地理空间的机器学习