About Test-time training for outlier detection

arXiv:2404.03495v1 Announce Type: new
Abstract: In this paper, we introduce DOUST, our method applying test-time training for outlier detection, significantly improving the detection performance. After thoroughly evaluating our algorithm on common benchmark datasets, we discuss a common problem and show that it disappears with a large enough test set. Thus, we conclude that under reasonable conditions, our algorithm can reach almost supervised performance even when no labeled outliers are given.

abstract algorithm arxiv benchmark cs.lg datasets detection discuss improving outlier paper performance set show test training type