Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Related tags

Deep Learning GJS
Overview

Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Environment Setup

Create conda environment, activate environment, and install additional pip packages

conda env create -f gjs_env.yml -n gjs
conda activate gjs
python -m pip install -r requirements.txt

Running Experiments

Please check scripts/ folder for yaml files corresponding to different experiments.

For example, to run JS on 40% symmetric noise on the full CIFAR-10 training set, run the following

python train.py -c scripts/C10/sym/js-40.yaml \
                --data_dir /path/to/dataset/

or GJS on 20% asymmetric noise on CIFAR-100

python train.py -c scripts/C100/asym/gjs-20.yaml \
                --data_dir /path/to/dataset/

or GJS on WebVision

python train.py -c scripts/WebVision/gjs.yaml \
                --data_dir /path/to/dataset/
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Comments
  • which loss should I use

    which loss should I use

    Hi, thank you for your outstanding contributions!

    I want to have a quick try using GJS on my own task, for 2-state and 3-state classification tasks, should I follow the hyper-parameters in C10/sym/js-40.yaml which using JSWCS loss and setting the js_weights to [0.7, 0.3]?

    Also, I have another regression task using MAE loss originally, which loss should I use in losses.py to replace the MAE loss with?

    Thank you very much.

    opened by yuzhiguo07 2
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