[Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)
-
Updated
Sep 3, 2024 - Python
[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)
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
Defending graph neural networks against adversarial attacks (NeurIPS 2020)
Official Implementation of Early-Learning Regularization Prevents Memorization of 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.
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)
Add a description, image, and links to the robust-learning topic page so that developers can more easily learn about it.
To associate your repository with the robust-learning topic, visit your repo's landing page and select "manage topics."