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[NeurIPS 2017] [ICML 2019] Code for complementary-label learning

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Complementary-Label Learning

This repository gives the implementation for complementary-label learning from the ICML 2019 paper [1], the ECCV 2018 paper [2], and the NeurIPS 2017 paper [3].

Requirements

  • Python 3.6
  • numpy 1.14
  • PyTorch 1.1
  • torchvision 0.2

Demo

The following demo will show the results with the MNIST dataset. After running the code, you should see a text file with the results saved in the same directory. The results will have three columns: epoch number, training accuracy, and test accuracy.

python demo.py -h

Methods and models

In demo.py, specify the method argument to choose one of the 5 methods available:

  • ga: Gradient ascent version (Algorithm 1) in [1].
  • nn: Non-negative risk estimator with the max operator in [1].
  • free: Assumption-free risk estimator based on Theorem 1 in [1].
  • forward: Forward correction method in [2].
  • pc: Pairwise comparison with sigmoid loss in [3].

Specify the model argument:

  • linear: Linear model
  • mlp: Multi-layer perceptron with one hidden layer (500 units)

Reference

  1. T. Ishida, G. Niu, A. K. Menon, and M. Sugiyama.
    Complementary-label learning for arbitrary losses and models.
    In ICML 2019.
    [paper]
  2. Yu, X., Liu, T., Gong, M., and Tao, D.
    Learning with biased complementary labels.
    In ECCV 2018.
    [paper]
  3. T. Ishida, G. Niu, W. Hu, and M. Sugiyama.
    Learning from complementary labels.
    In NeurIPS 2017.
    [paper]

If you have any further questions, please feel free to send an e-mail to: ishida at ms.k.u-tokyo.ac.jp.

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