MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks
Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data. In recent years, autoencoders have been shown to be effective anomaly detectors that train only...
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Zusammenfassung: | Anomaly detection is often considered a challenging field of machine learning
due to the difficulty of obtaining anomalous samples for training and the need
to obtain a sufficient amount of training data. In recent years, autoencoders
have been shown to be effective anomaly detectors that train only on "normal"
data. Generative adversarial networks (GANs) have been used to generate
additional training samples for classifiers, thus making them more accurate and
robust. However, in anomaly detection GANs are only used to reconstruct
existing samples rather than to generate additional ones. This stems both from
the small amount and lack of diversity of anomalous data in most domains. In
this study we propose MDGAN, a novel GAN architecture for improving anomaly
detection through the generation of additional samples. Our approach uses two
discriminators: a dense network for determining whether the generated samples
are of sufficient quality (i.e., valid) and an autoencoder that serves as an
anomaly detector. MDGAN enables us to reconcile two conflicting goals: 1)
generate high-quality samples that can fool the first discriminator, and 2)
generate samples that can eventually be effectively reconstructed by the second
discriminator, thus improving its performance. Empirical evaluation on a
diverse set of datasets demonstrates the merits of our approach. |
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DOI: | 10.48550/arxiv.1810.05221 |