Latent Space Autoregression for Novelty Detection
Open accessAbstract
Novelty detection is commonly referred as the discrimination of observations that do not conform to a learned model of regularity. Despite its importance in different application settings, designing a novelty detector is utterly complex due to the unpredictable nature of novelties and its inaccessibility during the training procedure, factors which expose the unsupervised nature of the problem. In our proposal, we design a general unsupervised framework where we equip a deep autoencoder with a parametric density estimator that learns the probability distribution underlying the latent representations with an autoregressive procedure. We show that a maximum likelihood objective, optimized in conjunction with the reconstruction of normal samples, effectively acts as a regularizer for the task at hand, by minimizing the differential entropy of the distribution spanned by latent vectors. In addition to providing a very general formulation, extensive experiments of our model on publicly available datasets deliver on-par or superior performances if compared to state-of-the-art methods in one-class and in video anomaly detection settings. Differently from our competitors, we remark that our proposal does not make any assumption about the nature of the novelties, making our work easily applicable to disparate contexts.
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- Authors
- Abati, D., Porrello, A., Calderara, S., & Cucchiara, R.
- Affiliations
- University of Modena and Reggio Emilia (IT)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 481-490
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2925312408
- Cited by
- 508
- References
- 3
- Citations by year
- 2026: 10 · 2025: 49 · 2024: 85 · 2023: 93 · 2022: 97 · 2021: 115 · 2020: 51 · 2019: 8
- FWCI
- 25.92
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- novelty detection, deep autoencoder, parametric density estimation, unsupervised learning, one-class classification, video anomaly detection, latent representations
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