SAGAN: Skip-Attention-GAN-anomaly

This repository contains PyTorch implementation of the following paper: SAGAN: SKIP-ATTENTION GAN FOR ANOMALY DETECTION.

You can get the paper from link

1. Installation

First clone the repository

git clone https://github.com/samet-akcay/skip-ganomaly.git

Create the virtual environment via conda
```
conda create -n sagan python=3.8
```
Activate the virtual environment.
```
conda activate sagan
```

Install the dependencies.

pip install --user --requirement requirements.txt

Install the SoftPool(you can see details in github link)

git clone https://github.com/alexandrosstergiou/SoftPool.git
cd SoftPool-master/pytorch
make install

3. Experiment

To replicate the results in the paper for CIFAR10 dataset, run the following commands:

# CIFAR
sh experiments/run_cifar.sh

4. Training

To list the arguments, run the following command:

python train.py -h

4.1. Training on CIFAR10

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                                                              \
        <airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck>    \
    --display                                   # optional if you want to visualize

4.2. Train on Custom Dataset

To train the model on a custom dataset, the dataset should be copied into ./data directory, and should have the following directory & file structure:

Custom Dataset
├── test
│   ├── 0.normal
│   │   └── normal_tst_img_0.png
│   │   └── normal_tst_img_1.png
│   │   ...
│   │   └── normal_tst_img_n.png
│   ├── 1.abnormal
│   │   └── abnormal_tst_img_0.png
│   │   └── abnormal_tst_img_1.png
│   │   ...
│   │   └── abnormal_tst_img_m.png
├── train
│   ├── 0.normal
│   │   └── normal_tst_img_0.png
│   │   └── normal_tst_img_1.png
│   │   ...
│   │   └── normal_tst_img_t.png

Then model training is the same as the training explained above.

python train.py                     \
    --dataset <name-of-the-data>    \
    --isize <image-size>            \
    --niter <number-of-epochs>      \
    --display                       # optional if you want to visualize

For more training options, run python train.py -h.

5. Citing Skip-Attention-GANomaly

If you use this repository or would like to refer the paper, please use the following BibTeX entry

@INPROCEEDINGS{9506332,
  author={Liu, Guoliang and Lan, Shiyong and Zhang, Ting and Huang, Weikang and Wang, Wenwu},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
  title={SAGAN: Skip-Attention GAN For Anomaly Detection}, 
  year={2021},
  volume={},
  number={},
  pages={2468-2472},
  doi={10.1109/ICIP42928.2021.9506332}}

SYLan2019 / Skip-Attention-GAN