Title: ANOMALY DETECTION AND LOCALIZATION IN IM-AGES USING GUIDED ATTENTION

Abstract: Anomaly detection and localization is a popular computer vision problem which involves detecting anomalous images and localizing anomalies within them. However, this task is challenging due to small sample size and pixel coverage of the anomaly in real-world scenarios. Previous works have a drawback of using anomalous images to compute a threshold during training to detect and localize anomalies. To tackle these issues, we propose AVAGA -the first end-to-end trainable convolutional adversarial variational autoencoder (CAVAE) framework using guided attention which localizes the anomaly with the help of attention maps. AVAGA detects an image as anomalous from the large pixel-wise difference between the input and reconstructed image. In an unsupervised setting, we propose a guided attention loss, where we encourage AVAGA to focus on all non-anomalous regions in the image without using any anomalous images during training. Furthermore, we also propose a selective gradient backpropagation technique for guided attention, which enhances the performance of anomaly localization while using few anomalous images in a weakly supervised setting. AVAGA outperforms the state-of-the-art (SoTA) methods for anomaly detection and localization.

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Peer review (iclr2020):
This paper addresses a method for anomalous image detection as well as anomalous region localization. The method employs the convolutional adversarial variational autoencoder for anomaly detection and uses guided attention (AVAGA) to localize the anomalous region in the image. Adversarial autoencoder and attention map are jointly trained in the end-to-end manner. Unsupervised and weakly-supervised settings are presented. ---Strength--- Most of existing methods need to tune the threshold for anomaly score using a small number of abnormal validation samples. The method in this paper bypasses the tuning of the threshold. Instead, only normal samples are used to learn an attention map to localize the anomaly. ---Weakness--- Two main ingredients in the proposed method (CAVAE and attention map via Grad-CAM) are known techniques, although such combination is a new interesting approach to anomaly detection and localization.