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arXiv · 1801.05365

Learning Deep Features for One-Class Classification

Pramuditha Perera·Vishal M. Patel
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Abstract

We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection and mobile active authentication datasets show that the proposed Deep One-Class (DOC) classification method achieves significant improvements over the state-of-the-art.

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BibTeXRIS

Pramuditha Perera, Vishal M. Patel. 2018-01-16. Learning Deep Features for One-Class Classification. https://doi.org/10.1109/tip.2019.2917862

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