[CVPR 2019] Pytorch codes for Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
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Updated
Dec 29, 2019 - Python
[CVPR 2019] Pytorch codes for Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)
OOD Generalization and Detection (ACL 2020)
Code for EMNLP-IJCNLP 2019 MRQA Workshop Paper: "Domain-agnostic Question-Answering with Adversarial Training"
A More Robust Domain Feature Decoupling Network. <Huawei 2020 2nd Artificial Intelligence Innovation Competition>
The code for the paper "Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study"
"Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).
The code for magnification generalization for the histopathology image embedding
An unofficial implementation of Frustratingly Simple Domain Generalization via Image Stylization
Code for ICLR2021 paper "Robust and Generalizable Visual Representation Learning via Random Convolutions"
ImageNet-R(endition) and DeepAugment (ICCV 2021)
CrossNorm and SelfNorm for Generalization under Distribution Shifts, ICCV 2021
[ICCV 2021] PyTorch implementation of "Universal Cross-Domain Retrieval: Generalizing across Classes and Domains"
This repository is an unofficial implementation in PyTorch for Learning to Generate Novel Domains for Domain Generalization
[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics
A PyTorch toolbox for domain adaptation, domain generalization, federated learning DA/DG, active learning DA/DG, ALDG and semi-supervised learning DA/DG.
[ICLR'22] Self-supervised learning optimally robust representations for domain shift.
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