Learning Deep Features for One-Class Classification
This work addresses the challenge of effective feature representation in one-class classification, which is crucial for applications like anomaly detection, but it appears incremental as it builds on existing CNN architectures.
The paper tackles the problem of feature learning for one-class classification by proposing a deep learning method that produces descriptive features with low intra-class variance, achieving significant improvements over state-of-the-art methods on anomaly detection, novelty detection, and mobile authentication datasets.
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.