Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection
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Computer Science > Machine Learning
Title:Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection
Abstract:Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection. We show that this protocol can become ill-posed when the held-out anomaly class overlaps the normal mixture in representation space. In this regime, anomaly scores may collapse toward chance or even invert, and the preferred score direction can depend on the unknown anomaly class. We introduce a simple training-free diagnostic, neighborhood class leakage, and show that it predicts score-direction instability across Fashion-MNIST, CIFAR-10, and Imagenette, in both pixel and VAE latent spaces. Our results suggest that class-split AD benchmarks should be treated as geometry-dependent stress tests rather than unconditional evidence of anomaly-detection ability.
| Comments: | 4+1 pages, 1 figure, accepted at ICML 2026 Workshop on Hypothesis Testing |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.02601 [cs.LG] |
| (or arXiv:2606.02601v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.02601
arXiv-issued DOI via DataCite
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Submission history
From: Alejandro Ascárate [view email][v1] Sat, 23 May 2026 06:27:42 UTC (114 KB)
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