Weakly Supervised Anomaly Detection

Weakly supervised anomaly detection aims to identify unusual patterns in data using only a small number of labeled anomalies, a significant improvement over fully unsupervised methods. Current research focuses on developing models that handle multimodal data distributions and incorporate additional knowledge sources, such as expert rules or implicit guidance, to improve generalization. These advancements leverage techniques like deep variational mixture models, counterfactual generation, and graph convolutional networks, leading to improved accuracy in diverse applications such as medical image analysis, video surveillance, and astrophysical data analysis. The resulting methods are valuable for scenarios where obtaining fully labeled data is expensive or impractical.