Neural Autoregressive Refinement for Self-Supervised Outlier Detection beyond Images
Abstract: Many self-supervised methods have been proposed with the target of image anomaly detection. These methods often rely on the paradigm of data augmentation with predefined transformations. However, it is not straightforward to apply these techniques to non-image data, such as time series or tabular data. Here we propose a novel data refinement (DR) scheme that relies on neural autoregressive flows (NAF) for self-supervised anomaly detection. Flow-based models allow to explicitly learn the probability density and thus can assign accurate likelihoods to normal data which makes it usable to detect anomalies. The proposed NAF-DR method is achieved by efficiently generating random samples from latent space and transforming them into feature space along with likelihoods via invertible mapping. The samples with lower likelihoods are selected and further checked by outlier detection using Mahalanobis distance. The augmented samples incorporated with normal samples are used to train a better detector to approach decision boundaries. Compared with random transformations, NAF-DR can be interpreted as a likelihood-oriented data augmentation that is more efficient and robust. Extensive experiments show that our approach outperforms existing baselines on multiple tabular and time series datasets, and {\color{blue}one real-world application}, significantly improving accuracy and robustness over the state-of-the-art baselines.
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