In this paper, we propose a self-supervised approach for tumor segmentation.
Based on this object function we introduce a novel information theoretic framework for unsupervised image anomaly detection.
Ranked #4 on Anomaly Detection on One-class CIFAR-10
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided.
Ranked #14 on Anomaly Detection on One-class CIFAR-10
We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors.
Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection.