EAL-GAN: Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning

This is the official implementation of “Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning”. Our paper has been officially accpted by IEEE Transactions on Pattern Analysis and Machine Intelligence, and a Preprint version of the manuscript can be found in Arxiv. If you use the code in this repo, please cite the paper as follows:

@ARTICLE{chen202-EALGAN,
     author={Chen, Zhi and Duan, Jiang and Kang, Li and Qiu, Guoping},
     journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
     title={Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning},
     year={2023},
     volume={45},
     number={6},
     pages={{7781-7798},
     doi={10.1109/TPAMI.2022.3225476}}

    We have implemented two versions of the proposed EAL-GAN, including:(1) one for classical anomaly detection datastes, whose codes are in the "EAL-GAN" folder, and (2) one for the image datasets, whose codes are in the "EAL-GAN-image" folder. Please note the EAL-GAN-image is sensitive to the learning rate and initiation. We print the changes in the loss of discriminators and generator during training, and if the loss become NAN in the first epoch, you should re-run the code with smaller learning rate. If that doesn't happen, you can expect a promising result.

Requirements

Pytorch >1.6
Python 3.7

Getting started

(1) You can run the script “train_EAL_GAN.py” to train the model proposed in our paper.
(2) Some of the datasets in our paper are given in the folder “/data”.
(3) Models/EAL-GAN.py is the proposed model.
(4) Models/losses.py is the loss functions.

Acknowledgments

some of our codes (e.g., Spectral Normalization) are extracted from the PyTorch implementation of BigGAN.