S2E

ICML'20: Searching to Exploit Memorization Effect in Learning from Corrupted Labels (PyTorch implementation).

This is the code for the paper: Searching to Exploit Memorization Effect in Learning from Corrupted Labels Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Kwok.

Requirements

Python = 3.7, PyTorch = 1.3.1, NumPy = 1.18.5, SciPy = 1.4.1 All packages can be installed by Conda.

Running S2E on benchmark datasets

Example usage for MNIST with 50% symmetric noise

python heng_mnist_main.py --noise_type symmetric --noise_rate 0.5 --num_workers 1 --n_iter 10 --n_samples 6

CIFAR-10 with 50% symmetric noise

python heng_main.py --noise_type symmetric --noise_rate 0.5 --num_workers 1 --n_iter 10 --n_samples 6

And CIFAR-100 with 50% symmetric noise

python heng_100_main.py --noise_type symmetric --noise_rate 0.5 --num_workers 1 --n_iter 10 --n_samples 6

Or see scripts (.sh files) for a quick start.

Citation

If you find this work helpful for your research, please cite the following paper:

@inproceedings{s2e2020icml,
  title={Searching to Exploit Memorization Effect in Learning from Corrupted Labels},
  author={Yao, Quanming and Yang, Hansi and Han, Bo and Niu, Gang and Kwok, James},
  booktitle={International Conference on Machine Learning},
  year={2020}
}

@TechReport{yao2018taking,
  author      = {Yao, Quanming and Wang, Mengshuo},
  institution = {arXiv preprint},
  title       = {Taking Human out of Learning Applications: A Survey on Automated Machine Learning},
  year        = {2018},
}

Relavent resources

A comprehensive survey on AutoML from our group is here.
Implementation of Co-teaching (the most important baseline in S2E).

Example applications

S2E (AutoML version of Co-teaching) is based on the small-loss trick and the memorization effect, the following examples have applied these princeples in various applications

Zhang et.al. Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis. Medical Imaging meets NeurIPS 2019.
Luo et.al. Deep Mining External Imperfect Data for Chest X-ray Disease Screening. IEEE Transactions on Medical Imaging. 2020.
Yang et.al. Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification. AAAI 2020.
Li et.al. Learning from Noisy Anchors for One-stage Object Detection. CVPR 2020.
Wang et.al. Co-Mining: Deep Face Recognition with Noisy Labels. ICCV 2020.

New Opportunities

Interns, research assistants, and researcher positions are available. See requirement

jerermyyoung/rtlearning