robust-learning

weijiaheng/Advances-in-Label-Noise-Learning

A curated (most recent) list of resources for Learning with Noisy Labels

shengliu66/ELR

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

JieyuZ2/wrench

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

HanxunH/Active-Passive-Losses

[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels

AndrewZhou924/Awesome-model-inversion-attack

A curated list of resources for model inversion attack (MIA).

mims-harvard/GNNGuard

Defending graph neural networks against adversarial attacks (NeurIPS 2020)

yunyuntsai/Black-box-Adversarial-Reprogramming

Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (ICML 2020)

chenpf1025/IDN

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

chenpf1025/RobustnessAccuracy

AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

ykwon0407/wdro_local_perturbation

Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)

monk1337/Awesome-Robust-Machine-Learning

A curated list of Robust Machine Learning papers/articles and recent advancements.

ch-shin/awesome-data-poisoning

zealscott/SGDL

Source code for Self-Guided Learning to Denoise for Robust Recommendation. SIGIR 2022.

dmizr/phuber

[Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)

NJUyued/RDA4RobustSSL

"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)

nthu-datalab/Runtime-Masking-and-Cleansing

Code for "Adversarial Robustness via Runtime Masking and Cleansing" (ICML 2020)

fchamroukhi/MEteorits

Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

tahmid0007/Low_Pass_ReLU

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.

XinshaoAmosWang/XinshaoAmosWang.github.io

Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI

e-wxy/NL-Sparse-Regularization

Robust learning on ISIC 2018, based on Learning with Noisy Labels via Sparse Regularization (ICCV 2021).