This repository contains a PyTorch implementation of the method presented in "Classification-Based Anomaly Detection for General Data" by Liron Bergman and Yedid Hoshen, ICLR 2020.
- Python 3 +
- Pytorch 1.0 +
- Tensorflow 1.8.0 +
- Keras 2.2.0 +
- sklearn 0.19.1 +
To replicate the results of the paper on the tabular-data:
python train_ad_tabular.py --n_rots=64 --n_epoch=25 --d_out=64 --ndf=32 --dataset=kdd
python train_ad_tabular.py --n_rots=256 --n_epoch=25 --d_out=128 --ndf=128 --dataset=kddrev
python train_ad_tabular.py --n_rots=256 --n_epoch=1 --d_out=32 --ndf=8 --dataset=thyroid
python train_ad_tabular.py --n_rots=256 --n_epoch=1 --d_out=32 --ndf=8 --dataset=arrhythmia
To replicate the results of the paper on CIFAR10:
python train_ad.py --m=0.1
If you find this useful, please cite our paper:
@inproceedings{bergman2020goad,
author = {Liron Bergman and Yedid Hoshen},
title = {Classification-Based Anomaly Detection for General Data},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2020}
}