GAN-Ensemble-for-Anomaly-Detection
This repository contains PyTorch implementation of the following paper: "GAN-Ensemble-for-Anomaly-Detection"
0. Environment Setup
pip install -r requirements.txt
1. Table of Contents
2. Experiment
To replicate the results in the paper for MNIST and CIFAR10 dataset, run the following commands:
#MNIST
sh experiments/run_mnist_en_fanogan.sh
sh experiments/run_mnist_en_egbad.sh
# CIFAR
sh experiments/run_cifar_en_fanogan.sh
sh experiments/run_cifar_en_egbad.sh
#OCT
sh experiments/run_oct_en_fanogan.sh
#KDD99
sh experiments/run_oct_en_egbad.sh
3. Training
To list the arguments, run the following command:
python train.py -h
To train the model on MNIST dataset for a given anomaly class, run the following:
python train.py \
--dataset mnist \
--niter <number-of-epochs> \
--abnormal_class <0,1,2,3,4,5,6,7,8,9> \
--setting <model-name: f-anogan, egbad, ganomaly, skipgan> \
--n_G <number of ensemble generators> \
--n_D <number of ensemble discriminators> \
To train the model on CIFAR10 dataset for a given anomaly class, run the following:
python train.py \
--dataset cifar10 \
--niter <number-of-epochs> \
--abnormal_class \
<0-9 for :airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck> \
--setting <model-name: f-anogan, egbad, ganomaly, skipgan> \
--n_G <number of ensemble generators> \
--n_D <number of ensemble discriminators> \
To train the model on OCT dataset for a given anomaly class, run the following:
python train.py \
--dataset OCT \
--niter <number-of-epochs> \
--setting <model-name: f-anogan, egbad, ganomaly, skipgan> \
--n_G <number of ensemble generators> \
--n_D <number of ensemble discriminators> \
To train the model on KDD99 dataset for a given anomaly class, run the following:
python train.py \
--dataset KDD99 \
--niter <number-of-epochs> \
--setting <model-name: f-anogan, egbad> \
--n_G <number of ensemble generators> \
--n_D <number of ensemble discriminators> \