deep-learning-architectures-and-techniques.md

CVPR-2023-Papers

Application
New collections

Deep Learning Architectures and Techniques

Title	Repo	Paper	Video
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Top-Down Visual Attention from Analysis by Synthesis
CUF: Continuous Upsampling Filters
Curvature-Balanced Feature Manifold Learning for Long-tailed Classification	➖		➖
Neighborhood Attention Transformer
Progressive Random Convolutions for Single Domain Generalization	➖		➖
Domain Expansion of Image Generators			➖
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization			➖
Boosting Verified Training for Robust Image Classifications via Abstraction
Joint Token Pruning and Squeezing Towards more Aggressive Compression of Vision Transformers
PointListNet: Deep Learning on 3D Point Lists	➖		➖
Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
Deep Graph Reprogramming	➖		➖
ConvNeXt V2: Co-Designing and Scaling ConvNets with Masked Autoencoders
Frustratingly Easy Regularization on Representation can Boost Deep Reinforcement Learning
Unified Pose Sequence Modeling	➖
RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer			➖
Real-Time Neural Light Field on Mobile Devices			➖
Towards Scalable Neural Representation for Diverse Videos
AutoFocusFormer: Image Segmentation Off the Grid
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation			➖
Deep Learning of Partial Graph Matching via Differentiable Top-K
WIRE: Wavelet Implicit Neural Representations
Decompose, Adjust, Compose: Effective Normalization by Playing with Frequency for Domain Generalization			➖
Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval	➖		➖
UniHCP: A Unified Model for Human-Centric Perceptions
Trainable Projected Gradient Method for Robust Fine-Tuning
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
B-Spline Texture Coefficients Estimator for Screen Content Image Super-Resolution
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint	➖
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention
On the Pitfall of Mixup for Uncertainty Calibration
Edges to Shapes to Concepts: Adversarial Augmentation for Robust Vision	➖
Mod-Squad: Designing Mixtures of Experts as Modular Multi-Task Learners
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network
PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers
BiFormer: Vision Transformer with Bi-Level Routing Attention
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection			➖
Hierarchical Neural Memory Network for Low Latency Event Processing
Block Selection Method for using Feature Norm in Out-of-Distribution Detection
NAR-Former: Neural Architecture Representation Learning Towards Holistic Attributes Prediction
MDL-NAS: A Joint Multi-Domain Learning Framework for Vision Transformer	➖
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Multi-Agent Automated Machine Learning	➖
Making Vision Transformers Efficient from a Token Sparsification View
Integral Neural Networks
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving			➖
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation			➖
One-Shot Model for Mixed-Precision Quantization	➖
Learning Dynamic Style Kernels for Artistic Style Transfer	➖
SVGformer: Representation Learning for Continuous Vector Graphics using Transformers	➖
How to Prevent the Continuous Damage of Noises to Model Training?	➖
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task
Differentiable Architecture Search with Random Features	➖		➖
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits	➖		➖
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers			➖
Token Turing Machines
Co-Training 2L Submodels for Visual Recognition	➖		➖
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search	➖
SLACK: Stable Learning of Augmentations with Cold-Start and KL Regularization
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins			➖
Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations
Detection of Out-of-Distribution Samples using Binary Neuron Activation Patterns
Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives
Superclass Learning with Representation Enhancement
Perception and Semantic Aware Regularization for Sequential Confidence Calibration
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
Improving Robustness of Vision Transformers by Reducing Sensitivity to Patch Corruptions			➖
E2PN: Efficient SE(3)-Equivariant Point Network
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation
Regularization of Polynomial Networks for Image Recognition			➖
Hyperspherical Embedding for Point Cloud Completion
On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data
Independent Component Alignment for Multi-Task Learning
MP-Former: Mask-Piloted Transformer for Image Segmentation
SMPConv: Self-Moving Point Representations for Continuous Convolution
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset
FlexiViT: One Model for All Patch Sizes
GEN: Pushing the Limits of Softmax-based Out-of-Distribution Detection
Zero-Shot Noise2Noise: Efficient Image Denoising without Any Data
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference
HNeRV: A Hybrid Neural Representation for Videos
Re-Basin via Implicit Sinkhorn Differentiation
Bayesian Posterior Approximation with Stochastic Ensembles			➖
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Enhancing Multiple Reliability Measures via Nuisance-Extended Information Bottleneck			➖
Federated Learning with Data-Agnostic Distribution Fusion