One-Class Classification with Deep Adversarial Learning

Abstract

One-class classification (OCC) seeks to build a machine-learning model when the negative class is either absent, poorly sampled, or not well defined. In this paper, we present a deep adversarial learning based architecture for one-class classification. Our architecture is composed of two deep neural networks, a generator and a discriminator, that are competing while collaborating with each other since it is inspired by the success of Generative Adversarial Networks (GANs). The generator network contains a Deconvolutional Neural Networks (aka. decoder), which is trained using a zero-centered Gaussian noise as the feature representation of the pseudo-negative class to learn a good representation as well as the boundary for the classifiable distortion of the target (or positive) class with the assistance from the target class. The outputs produced by the generator network are aggregated with the real positive class data samples, which are then used to train the discriminator network, whose goal is to understand the underlying concept in the positive class, and then classify the negative testing samples. The proposed architecture applies to a variety of OCC problems such as novelty detection, anomaly detection, and mobile user authentication. The experiments on MNIST and Caltech-256 images demonstrate that our architecture achieves superior results over the recent state-of-the-art approaches.