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