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In recent years, there is an increasing interests in reconstruction basedgenerative models for image One-Class Novelty Detection, most of which onlyfocus on image-level information. While in this paper, we further exploit thelatent space of Variational Auto-encoder (VAE), a typical reconstruction basedmodel, and we innovatively divide it into three regions:Normal/Anomalous/Unknown-semantic-region. Based on this hypothesis, we proposea new VAE architecture, Recoding Semantic Consistency Based VAE (RSC-VAE),combining VAE with recoding mechanism and constraining the semantic consistencyof two encodings. We come up with three training modes of RSC-VAE: 1. One-ClassTraining Mode, alleviating False Positive problem of normal samples; 2.Distributionally-Shifted Training Mode, alleviating False Negative problem ofanomalous samples; 3. Extremely-Imbalanced Training Mode, introducing a smallnumber of anomalous samples for training to enhance the second mode. Theexperimental results on multiple datasets demonstrate that our mechanismachieves state-of-the-art performance in various baselines including VAE.

RSC-VAE: Recoding Semantic Consistency Based VAE for One-Class Novelty Detection