About Test-time training for outlier detection
<|reference_start|>About Test-time training for outlier detection: 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.<|reference_end|>
@article{klüttermann2024about,
    title={About Test-time training for outlier detection},
    author={Simon Kl"uttermann and Emmanuel M"uller},
    journal={arXiv preprint arXiv:2404.03495},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03495},
    primaryClass={cs.LG}
}