Anomaly Detection, Novelty Detection, Out-of-Distribution Detection
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We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework.
Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence.
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable.
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
A new spatial-temporal area under curve (STAUC) evaluation metric is proposed and used with DoTA.
Deep learning, one of the most successful AI techniques, is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can be critical for efficient and reliable COVID-19 screening.
For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains.
First, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images.