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

arXiv 2305.04275 ↗First posted 7 May 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 7 May 2023.

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23 of 38 references.

References · 23

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
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  11. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  12. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
11 more
  1. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  2. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  3. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  4. 2020Classification-Based Anomaly Detection for General Datacited by 211
  5. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  6. 2020Explainable Deep One-Class Classificationcited by 127
  7. 2016Deep Structured Energy Based Models for Anomaly Detectioncited by 114
  8. 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
  9. 2020Backpropagated Gradient Representations for Anomaly Detectioncited by 54
  10. 2018Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoderscited by 49
  11. 2019Attention Guided Anomaly Localization in Imagescited by 9

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