Anomaly Detection Requires Better Representations
Machine Learning
2022-10-20 v1 Computer Vision and Pattern Recognition
Abstract
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection. In this position paper, we first explain how self-supervised representations can be easily used to achieve state-of-the-art performance in commonly reported anomaly detection benchmarks. We then argue that tackling the next generation of anomaly detection tasks requires new technical and conceptual improvements in representation learning.
Cite
@article{arxiv.2210.10773,
title = {Anomaly Detection Requires Better Representations},
author = {Tal Reiss and Niv Cohen and Eliahu Horwitz and Ron Abutbul and Yedid Hoshen},
journal= {arXiv preprint arXiv:2210.10773},
year = {2022}
}
Comments
Accepted to ECCV SSLWIN Workshop (2022)