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Learning with Feature-Dependent Label Noise: A Progressive Approach (ICLR 2021, spotlight) Paper

Requirements

• PyTorch 1.6 (Other versions >= 1.0 should also work)
• Python 3.8.5 (Other Python 3.x should also work)
• tqdm, termcolor, etc (which can be easily installed via pip)

Usage

• The folder cifar contains the code for generating PMD noise and running on synthetic datasets.
• The folder clothing1m contains the code for running on Clothing1M dataset.
• The folder Food101 contains the code for running on Food-101N dataset.

Reference

@inproceedings{prog_noise_iclr2021,
  title={Learning with Feature-Dependent Label Noise: A Progressive Approach},
  author={Zhang, Yikai and Zheng, Songzhu and Wu, Pengxiang and Goswami, Mayank and Chen, Chao},
  booktitle={ICLR},
  year={2021}
}

Related Work

• Error-Bounded Correction of Noisy Labels. In ICML, 2020. [Paper][Code]
• A Topological Filter for Learning with Label Noise. In NeurIPS, 2020. [Paper][Code]