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