Novelty detection using ensembles with regularized disagreement.
Year: 2020
Type: preprint
Abstract: Despite their excellent performance on in-distribution (ID) data, machine learning-based prediction systems often predict out-of-distribution (OOD) samples incorrectly while indicating high confidence. Instead, they should flag samples that are not similar to the training data, for example, when new classes emerge over time. Even though current OOD detection algorithms can successfully distinguish completely different data sets, they fail to reliably identify samples from novel classes. We develop a new ensemble-based procedure that promotes model diversity and exploits regularization to limit disagreement to only OOD samples, using a batch containing an unknown mixture of ID and OOD data. We show that our procedure significantly outperforms state-of-the-art methods, including those that have access, during training, to data that is known to be OOD. We run extensive comparisons of our approach on a variety of novel-class detection scenarios, on standard image data sets such as SVHN/CIFAR-10/CIFAR-100, as well as on new disease detection on medical image data sets.
Source: arXiv (Cornell University)
Authors: Alexandru Țifrea, Eric Stavarache, Fanny Yang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3198218876