  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.
