Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection has become more and more popular. In this paper, a new domain-based anomaly detection method based on generative adversarial networks (GAN) is proposed. Minimum likelihood regularization is proposed to make the generator produce more anomalies and prevent it from converging to normal data distribution. Proper ensemble of anomaly scores is shown to improve the stability of discriminator effectively. The proposed method has achieved significant improvement than other anomaly detection methods on Cifar10 and UCI datasets.
Bad-GAN: In @cite_24 , KL-divergence is used to disturb the generator of GAN to achieve better performance on semi-supervised problem. Although the motivation is the same as ours, the construction of KL-divergence is different from ours. KL-divergence in @cite_24 aims to minimize the distance between the probability induced by generator and a constructed distribution. In this paper, we use KL-divergence to make the generator have low probability values (i.e., minimize the likelihood) on normal data via variational inference. We show that minimum likelihood regularization is effective in anomaly detection.
@cite_24
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Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets.