Peer Loss functions

This repository is the (Multi-Class & Deep Learning) Pytorch implementation of “Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates” accepted by ICML2020.

Required Packages & Environment

Supported OS: Windows, Linux, Mac OS X; Python: 3.6/3.7;

Deep Learning Library: PyTorch (GPU required)

Required Packages: Numpy, Pandas, random, sklearn, tqdm, csv, torch (Keras is required if you want to estimate the noise transition matrix).


This repository includes:

? Multi-class implementation of Peer Loss functions;

? Peer Loss functions in Deep Learning;

? Dynamical tunning strategies of Peer Loss functions to further improve the performance.

Details of running (equationweighted) Peer Loss functions on MNIST, Fashion MNIST, CIFAR-10, CIFAR-100 with different noise setting are mentioned in the file in each folder.

The workflow of equationweighted Peer Loss functions comes to:


Decision boundary visualization

Given a 2D syntheric dataset, the decision boundaries returned by training with Cross-entropy loss become loose when the noise rate is high. However, the decision boundaries w.r.t. Peer Loss functions remain tight despite high presence of label noise.



If you use our code, please cite the following paper:

  title={Peer loss functions: Learning from noisy labels without knowing noise rates},
  author={Liu, Yang and Guo, Hongyi},
  booktitle={International Conference on Machine Learning},

Related Code

? Peer Loss functions and its experiments on UCI datasets is available at:

? The following work extends Peer Loss functions to a family of divergence-type robust loss functions and is available at:
The corresponding paper is “When Optimizing f-Divergence is Robust with Label noise” accepted by ICLR2021.

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