Awesome

Few-shot Learning with Noisy Labels

Authors: Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, Tal Hassner

This repository is the official PyTorch implementation of the CVPR 2022 paper Few-shot Learning with Noisy Labels.

Citation

If you find any part of our paper or this codebase useful, please consider citing our paper:

@inproceedings{liang2022few,
  title={Few-shot learning with noisy labels},
  author={Liang, Kevin J and Rangrej, Samrudhdhi B and Petrovic, Vladan and Hassner, Tal},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9089--9098},
  year={2022}
}

License

Please see LICENSE.md for more details.

Acknowledgements

This codebase was built starting from the learn2learn library for meta-learning, in particular for setting up baseline non-noisy few-shot methods.

Set-up

Environment

For requirements, please see environment.yml. Experiments in the paper were run with Python 3.9.6 and Pytorch 1.9.0; other versions of Python and PyTorch may work, though are untested.

conda env create --file environment.yml

Datasets

Place datasets in the directory ./data. MiniImageNet and TieredImageNet datasets should download automatically when run for the first time if they do not exist already. If running experiments with ImageNet outlier noise, you will need to generate the outlier dataset from the ImageNet training set. See create_miniimagenet_outliers.py for how to do so.

Training and Evaluation

The script main.py trains a model and then runs evaluation at the specified noise levels. Running just evaluation with this script can be done by simply setting the number of train epochs to 0 (--max-epoch 0).

This codebase uses Visdom for visualizing training/eval curves. Although not required for the script to run, a Visdom server can be initialized with:

visdom

Training backbone

For all models, we follow the common practice of first pre-training the backbone and then freezing it. An example command for training the conv4 backbone for MiniImageNet:

bash scripts/train_backbone.sh

This script will train the conv4 backbone and then evaluate it at the specified noise levels and noise type. This represents the performance of ProtoNet at various noise levels. Various other few-shot baselines can be run by loading this backbone and running evaluation on noisy support sets.

Training/Evaluating TraNFS

In our paper, we propose a Transformer for Noisy Few-Shot (TraNFS), which learns a dynamic noise filtering mechanism for noisy support sets. An example command for launching TraNFS training for symmetric label swap noise on MiniImageNet:

bash scripts/train_transf.sh

After training the model, this script will also evaluate the model at the specified noise levels and noise type.