4 papers
Anomaly-Free Self-Optimization via AUC Bounds
Kevin Wilkinghoff, Zheng-Hua Tan
Anomalies are rare, and anomalous data are often unavailable during development, making it difficult to determine which anomaly detection models and configurations will generalize…
Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?
Kevin Wilkinghoff, Zheng-Hua Tan
Anomaly detection systems are often trained using normal data alone, while model selection and evaluation typically require labeled anomalies. We study whether anomaly detection pe…
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…
Temporal Pooling Strategies for Training-Free Anomalous Sound Detection with Self-Supervised Audio Embeddings
Kevin Wilkinghoff, Sarthak Yadav, Zheng-Hua Tan
Training-free anomalous sound detection (ASD) based on pre-trained audio embedding models has recently garnered significant attention, as it enables the detection of anomalous soun…