Metric Learning for Novelty and Anomaly Detection

Abstract
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to...
Paper Details
Title
Metric Learning for Novelty and Anomaly Detection
Published Date
Aug 16, 2018
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