HLRAD: High-dimensional Latent Representation for Unified Anomaly Detection

Unified unsupervised anomaly detection aims to train a sin-gle model to detect and localize diverse anomalies across multi-class sam-ples, representing one of the most challenging tasks in anomaly detec-tion. Existing methods typically rely on highly compressing inputs intoa low-dimensional latent space to subsequently reconstruct anomalousfeatures. However, low-dimensional latent spaces critically limit infor-mation capacity, leading to unavoidable feature loss during compressionand significantly degrading the quality of reconstruction and represen-tational power of the model. Therefore, we indicate that dimensionalitycompression in latent space is not a necessary requirement for anomalydetection, and the editability introduced by compression is not inher-ently essential for models reconstructing anomalies. Thus, we propose anovel anomaly detection framework without latent space compression,called HLRAD (High-dimensional Latent Representation for UnifiedAnomaly Detection). Unlike prior methods, HLRAD innovatively en-ables dimension expansion rather than compression in the latent space,and the method effectively avoids feature loss from compression to ensurehigh-quality reconstruction while constructing a semantically enrichedhigh-dimensional latent representation space. Furthermore, by embed-ding the expanded high-dimensional semantic features into the latentrepresentation, HLRAD enables the model to more fully capture the se-mantic feature distribution of normal samples, significantly enhancingcomplex anomaly detection performance. We conducted extensive ex-periments on major anomaly detection benchmark datasets, includingMVTec-AD, VisA, and Real-IAD. In unified settings, HLRAD outper-forms state-of-the-art methods.