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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:

<pre>python main.py --exp [config_file]</pre>

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

50% symmetric noise: <pre>python main.py --exp cifar10_sym</pre>
40% asymmetric noise: <pre>python main.py --exp cifar10_asym</pre>

Citation

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

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