Learning from Noisy Data with Robust Representation Learning (ICCV 2021)

This is the PyTorch implementation of the ICCV paper [link].

Requirements:

• PyTorch = 1.4
• pip install tensorboard_logger torchnet faiss-gpu

Configuration:

Hyper-parameters and model configurations are located in ./config

Dataset:

In order to run experiments, please download the corresponding dataset and place it at the location specified in the config file.

Execution:

python main.py --exp [config_file]

For example, run the following command to reproduce the paper's result on CIFAR-10:

1. 50% symmetric noise:

   python main.py --exp cifar10_sym

2. 40% asymmetric noise:

   python main.py --exp cifar10_asym

Citation

If you find this code to be useful for your research, please consider citing.

@inproceedings{RRL,
      title={Learning from Noisy Data with Robust Representation Learning}, 
      author={Junnan Li and Caiming Xiong and Steven Hoi},
      year={2021},
      booktitle = {{ICCV}},
}