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Unsupervised anomaly detection with generative model, keras implementation

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query image generated similar image differece



AnoGAN keras implementation

Unsupervised anomaly detection with DCGAN

Requirements

Usage

First, check directory structure

├── main.py
├── anogan.py 
├── weights
    ├── discriminator.h5
    └── generator.h5
└── result
    └── save the generated images when training

To test this project

$ python main.py

To train a model

$ python main.py --mode train

Then, the training steps(image) will be saved 'result' directory


usage: main.py [-h] [--img_idx IMG_IDX] 
                    [--label_idx LABEL_IDX] 
                    [--mode MODE]

Reference

paper : https://arxiv.org/abs/1703.05921
AnoGAN(code, keras) : https://github.com/yjucho1/anoGAN
AnoGAN(code, tf) : https://github.com/LeeDoYup/AnoGAN

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