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Anomaly Detection with VAEGAN and Attention [JSAI2019 report]

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abstract of "Anomaly Detection with VAEGAN and Attention".

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Anomaly Detection with VAEGAN and Attention [JSAI2019 report]

  1. 1. Daiki Tanaka Kyoto University
  2. 2. Problem setting : Anomaly images detection by deep generative models • Background : Deep generative models, such as Auto-Encoder, GAN or VAE are used for detecting anomalous images. When testing images, reconstruction errors are used to detect anomalies. • Challenge : When detecting anomalous images by reconstruction error, noisy areas in images are misunderstood as anomalous. • Problem setting (Input, Output) : • Input : Image • Output : If the image is anomaly image, or not.
  3. 3. Solution : Using discriminator’s attention to correct noisy areas • Key idea : Discriminator has to focus on major areas of images to classify real images and generated images, not noisy areas. • Train : min max (KL + Reconstruction error + GAN loss) • Test : Taking reconstruction error between; • 1. Original image • 2. Reconstructed image * pixel-level attention weights (calculated by Grad-CAM)
  4. 4. Result : Outperform other deep generative based predictions • Evaluation method : ROC-AUC score prediction of anomalies • Data : MNIST + Added Noises (One class is normal, and the other 9 classes are anomalies.)

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