arXiv:2607. 22286v1 Announce Type: new Abstract: Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 02601v1 Announce Type: new Abstract: Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection.
arXiv:2505. 03509v3 Announce Type: replace Abstract: Anomaly detection in large datasets is essential in astronomy and computer vision.
arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.
arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.
arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.
arXiv:2509. 05663v4 Announce Type: replace Abstract: Truly unsupervised approaches for time series anomaly detection are rare in the literature.