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 describes an approach to detect and localize anomalies in images. A VAE based architecture is used with some fittings to 1) Sharpen blurry output of VAE (with an adversarial loss) 2) Detect and localize anomalies with what they call the guided attention loss (this is the principal novelty it appears) The guided attention loss is obtained from an externally trained network (Grad-CAM, Selvaraju et al). They consider two cases, one with *normal* (non anomalous) images, which is totally unsupervised, while the other case is weakly supervised with some images with anomalies in them, where the attention loss is designed appropriately to handle the two cases. Comparisons are made using 4 different datasets and metrics (e.g. IoU, accuracy of correctly classified anomalous images), and it is shown that their attention based model outperforms the state of the art. My thoughts: It makes sense, loosely speaking, that an attention based model would perform better than one without, enabling it to focus on regions of interest. The paper generally makes this point clear with results. However, I feel that the prior work is not very well described, and the basic idea that an AE architectures are usable for the anomaly detection task because its performance would be worse on images it has not seen (anomalies) is not immediately obvious. I am ambivalent about accepting this paper until we have a better coverage of related work.