Deep Weakly-supervised Anomaly Detection

30 Oct 2019Guansong PangChunhua ShenHuidong JinAnton van den Hengel

Anomaly detection is typically posited as an unsupervised learning task in the literature due to the prohibitive cost and difficulty to obtain large-scale labeled anomaly data, but this ignores the fact that a very small number (e.g.,, a few dozens) of labeled anomalies can often be made available with small/trivial cost in many real-world anomaly detection applications. To leverage such labeled anomaly data, we study an important anomaly detection problem termed weakly-supervised anomaly detection, in which, in addition to a large amount of unlabeled data, a limited number of labeled anomalies are available during modeling... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet