complementary-label-learning

This code gives the implementation of the paper ''Discriminative Complementary-Label Learning with Weighted Loss''.

Requirements

-Python 3.6

-PyTorch 1.1

or using Colab to implement

demo.py

This is main function. After running the code, you should see a text file with the results saved in the same directory. The results will have four columns: epoch number, training accuracy, test accuracy, train loss.

python demo.py --me <method name> --mo <model name>

Methods and models

In demo.py, specify the method argument to choose one of the 2 methods available:

-w_loss: L-W risk estimator is defined by Equation(8) in the paper

-non_k_softmax: L-UW loss is defined by Equation(7) in the paper

Specify the model argument:

-linear: linear model

-MLP: multi-layer perceptron with one hidden layer (500 units)

citation

@inproceedings{DBLP:conf/icml/GaoZ21, author = {Yi Gao and Min{-}Ling Zhang}, title = {Discriminative Complementary-Label Learning with Weighted Loss}, booktitle = {Proceedings of the 38th International Conference on Machine Learning, {ICML} 2021, 18-24 July 2021, Virtual Event}, volume = {139}, year = {2021} }