Mean-Shifted Contrastive Loss for Anomaly Detection

Official PyTorch implementation of “Mean-Shifted Contrastive Loss for Anomaly Detection”.

Virtual Environment

Use the following commands:

cd path-to-directory
virtualenv venv --python python3
source venv/bin/activate
pip install -r requirements.txt

Experiments

To replicate the results on CIFAR-10 for a specific normal class:

python main.py --dataset=cifar10 --label=n

Where n indicates the id of the normal class.

To replicate the results on CIFAR-10 with ResNet18 for a specific normal class:

python main.py --dataset=cifar10 --label=n --backbone=18

Where n indicates the id of the normal class.

Use the --angular flag to jointly optimize the mean-shifted contrastive loss and the angular center loss.

To run experiments on different datasets, please set the path in utils.py to the desired dataset.

Citation

If you find this useful, please cite our paper:

@article{reiss2021mean,
  title={Mean-Shifted Contrastive Loss for Anomaly Detection},
  author={Reiss, Tal and Hoshen, Yedid},
  journal={arXiv preprint arXiv:2106.03844},
  year={2021}
}