Y-GAN

Official implementation of the paper "Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection" ¹. Y-GAN is a one-class learning model designed for the detection of anomalies in images. The model is trained to disentangle semantically-relevant image information from irrelevant, residual characteristics and facilitates efficacious anomaly detection based on selective image encoding.

For more information please refer to the paper available here.

1. Install the dependencies

The model is implemented using PyTorch. The full list of used libraries can be found in requirements.txt.

pip install -r requirements.txt

2. Prepare the data

The dataset you are using for training or testing Y-GAN should have the following directory structure where the names of individual classes are represented with unique numbers or strings:

Dataset_name
├── test
│   ├── 0.normal
│   │   ├── 0
│   │   │   └── normal_train_class_0_img_0.png
│   │   │   └── normal_train_class_0_img_1.png
│   │   │   ...
|   │   ...
│   │   ├── n
│   │   │   └── normal_train_class_n_img_0.png
│   │   │   └── normal_train_class_n_img_1.png
│   │   │   ...
│   ├── 1.abnormal
│   │   ├── m
│   │   │   └── abnormal_test_class_m_img_0.png
│   │   │   └── abnormal_test_class_m_img_1.png
│   │   │   ...
├── train
│   ├── 0.normal
│   │   ├── 0
│   │   │   └── normal_test_class_0_img_0.png
│   │   │   └── normal_test_class_0_img_1.png
│   │   │   ...
|   │   ...

3. Training

To train Y-GAN on Dataset_name placed in a root directory named data, run the following:

python train.py --dataroot data --dataset Dataset_name --name experiment_name --isize image_size

4. Evaluation

To test a pretrained Y-GAN run the following:

python test.py --dataroot data --dataset Dataset_name --name experiment_name --isize image_size --load_checkpoint output/experiment_name/train/weights/y_gan.pth

5. Citing Y-GAN

If you find this code useful or you want to refer to the paper, please cite using the following BibTeX:

@article{ivanovska2024ygan,
      title={Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection}, 
      author={Marija Ivanovska and Vitomir Štruc},
      journal = {Expert Systems with Applications},
      volume = {248},
      pages = {123410},
      year = {2024},
      issn = {0957-4174},
      doi = {https://doi.org/10.1016/j.eswa.2024.123410}
      }

Acknowledgements

This code is partly based on the official implementation of skip-GANomaly.

References

Footnotes

1. M. Ivanovska, V. Struc, Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection, Expert Systems with Applications, 2024 ↩