Anomaly detection based on subspace learning has attracted much attention, in which the compactness of subspace is commonly considered as the core concern. Most related studies directly optimize the ...

Recently, autoencoder (AE)-based hyperspectral anomaly detection methods have demonstrated excellent performance on hyperspectral images (HSIs). The AE can simultaneously reconstruct both the anomaly ...

An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...

Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving Alexey Nekrasov 1, Malcolm Burdorf 1, Stewart Worrall 2, Bastian Leibe 1, Julie Stephany Berrio Perez 2 ...