Official PyTorch implementation of “Mean-Shifted Contrastive Loss for Anomaly Detection” (AAAI 2023).
Use the following commands:
cd path-to-directory
virtualenv venv --python python3
source venv/bin/activate
pip install -r requirements.txt
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.
See our new paper “Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection” which achieves state-of-the-art video anomaly detection performance on multiple benchmarks including 85.9% ROC-AUC on the ShanghaiTech dataset.
If you find this useful, please cite our paper:
@inproceedings{reiss2023mean,
title={Mean-shifted contrastive loss for anomaly detection},
author={Reiss, Tal and Hoshen, Yedid},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={2},
pages={2155--2162},
year={2023}
}