033-learning-from-noisy-data-with-robust-representation-learning

https://github.com/szu-advtech-2023/033-learning-from-noisy-data-with-robust-representation-learning

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Host: GitHub
Owner: SZU-AdvTech-2023
Language: Python
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Created over 2 years ago · Last pushed over 2 years ago

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Citation

https://github.com/SZU-AdvTech-2023/033-Learning-from-Noisy-Data-with-Robust-Representation-Learning/blob/main/

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

This is the reproduced version of the [original PyTorch implementation](https://github.com/salesforce/RRL) of the ICCV paper.

### 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}},
}

Owner

Name: SZU-AdvTech-2023
Login: SZU-AdvTech-2023
Kind: organization

Repositories: 1
Profile: https://github.com/SZU-AdvTech-2023

Citation (citation.txt)

@inproceedings{REPO033,
    author = "Li, Junnan and Xiong, Caiming and Hoi, Steven CH",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "9485--9494",
    title = "{Learning from Noisy Data with Robust Representation Learning}",
    year = "2021"
}

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