Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

arXiv:2306.03492v2 Announce Type: replace
Abstract: Anomaly Detection is challenging as usually only the normal samples are seen during training and the detector needs to discover anomalies on-the-fly. The recently proposed deep-learning-based approaches could somehow alleviate the problem but there is still a long way to go in obtaining an industrial-class anomaly detector for real-world applications. On the other hand, in some particular AD tasks, a few anomalous samples are labeled manually for achieving higher accuracy. However, this performance gain is …

abstract annotation anomaly anomaly detection arxiv budget class cs.cv detection fly industrial normal residual samples semi-supervised training transformer type