Toward a Realistic Benchmark for Out-of-Distribution Detection

arXiv:2404.10474v1 Announce Type: new
Abstract: Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set. A common approach to address this issue is to endow deep neural networks with the ability to detect OOD samples. Several benchmarks have been proposed to design and validate OOD detection techniques. However, many of them are based on far-OOD samples …

abstract arxiv benchmark cs.cv cs.lg detection distribution issue networks neural networks samples services set technologies training type