About Test-time training for outlier detection
Simon Klüttermann Emmanuel Müller
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.