A curated list of resources for Learning with Noisy Labels
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Updated
May 3, 2024
A curated list of resources for Learning with Noisy Labels
A curated (most recent) list of resources for Learning with Noisy Labels
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
Defending graph neural networks against adversarial attacks (NeurIPS 2020)
Source code for Self-Guided Learning to Denoise for Robust Recommendation. SIGIR 2022.
AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
[Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)
A curated list of resources for model inversion attack (MIA).
Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (ICML 2020)
Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)
AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS
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.
Robust learning on ISIC 2018, based on Learning with Noisy Labels via Sparse Regularization (ICCV 2021).
Code for "Adversarial Robustness via Runtime Masking and Cleansing" (ICML 2020)
Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI
"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)
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