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AutoEncoder regularization using Support Vector Data Description for Anomaly Detection

In computer vision, learning discriminative features to detect anomalies in images is a challenge. The majority of deep learning-based image anomaly detection approaches are compression-reconstruction or generation-based models that were not initially intended for the anomaly detection task. Only a few methods involve dedicated objective function to help detect anomalies and they are not visually explainable as well as reconstruction based approaches. Though the popular reconstruction-based approach for anomaly detection using Convolutional AutoEncoder has achieved the state of the art results, there is no provision to induce the fabrication of discriminatively learnt embeddings from the inputs to well reflect anomalies in the output. We propose an approach using Support Vector Data Description as a regularizer to enforce discriminative ability to easily segregate anomalies from normality with little effort in modelling and tuning the AutoEncoders in our work. We evaluate our approach on several visual anomaly detection datasets to show the capability of our approach. We also perform extensive ablation studies for efficient tuning of parameters.