Daiki Tanaka from Kyoto University proposes a method to detect anomaly images using deep generative models while correcting for noisy areas that could be misidentified as anomalies. The method trains an autoencoder with a GAN discriminator that learns to focus on major image areas rather than noise. At test time, it calculates the reconstruction error between the original and reconstructed image, weighted by the discriminator's attention weights to discount noisy pixels. On MNIST data with added noise, the method outperforms other deep generative models in anomaly detection as measured by ROC-AUC scores.

1. Daiki Tanaka
Kyoto University

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. 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. 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.)