There are 1 repository under robust-learning topic.
A curated list of resources for Learning with Noisy Labels
A curated (most recent) list of resources for Learning with Noisy Labels
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
A curated list of resources for model inversion attack (MIA).
Defending graph neural networks against adversarial attacks (NeurIPS 2020)
Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (ICML 2020)
AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
A curated list of Robust Machine Learning papers/articles and recent advancements.
Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)
"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)
Code for "Adversarial Robustness via Runtime Masking and Cleansing" (ICML 2020)
Corruption Robust Image Classification with a new Activation Function. Our proposed Activation Function is inspired by the Human Visual System and a classic signal processing fix for data corruption.
Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS
Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI
Robust learning on ISIC 2018, based on Learning with Noisy Labels via Sparse Regularization (ICCV 2021).