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How Does a Neural Network’s Architecture Impact Its Robustness to Noisy Labels?

This repository is the PyTorch implementation of the experiments in the following paper:

Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson, Jimmy Ba. How Does a Neural Network’s Architecture Impact Its Robustness to Noisy Labels? NeurIPS 2021.

arXiv

If you make use of the relevant code/experiment/idea in your work, please cite our paper (Bibtex below).

@article{li2021does,
  title={How does a Neural Network's Architecture Impact its Robustness to Noisy Labels?},
  author={Li, Jingling and Zhang, Mozhi and Xu, Keyulu and Dickerson, John and Ba, Jimmy},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

Requirements

• This codebase has been tested for python3.7 and pytorch 1.4.0 (with CUDA VERSION 10.0).
• The packages networkx and pytorch-geometric need to be installed separately. networkx and geometric versions can be decided based on pytorch and CUDA version.

Instructions

Refer to each folder for instructions to reproduce the experiments.

• Experiments related to graph algorithms are in the gnn folder.
• Experiments on image classification datasets are in the vision folder.