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Learning Deep Features for One-Class Classification - 2019

Learning Deep Features For One-Class Classification

Research Area:  Machine Learning


We present a novel deep-learning-based approach for one-class transfer learning in which labeled data from an unrelated task is used for 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.


Author(s) Name:  Pramuditha Perera, Vishal M. Patel

Journal name:  IEEE Transactions on Image Processing

Conferrence name:  

Publisher name:  ACM


Volume Information: