HRN: A Holistic Approach to One Class Learning

Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu

Abstract:

Existing neural network based one-class learning methods mainly use various forms
of auto-encoders or GAN style adversarial training to learn a latent representation
of the given one class of data. This paper proposes an entirely different approach
based on a novel regularization, called holistic regularization (or H-regularization),
which enables the system to consider the data holistically, not to produce a model
that biases towards some features. Combined with a proposed 2-norm instance
level data normalization, we obtain an effective one-class learning method, called
HRN. To our knowledge, the proposed regularization and the normalization method
have not been reported before. Experimental evaluation using both benchmark
image classification and traditional anomaly detection datasets show that HRN
markedly outperforms the state-of-the-art existing deep/non-deep learning models.
The code of HRN can be found here.
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