Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
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Updated
May 22, 2023 - Python
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
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
Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)
Source code for Self-Guided Learning to Denoise for Robust Recommendation. SIGIR 2022.
[Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)
"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)
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