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Title: Anomaly Detection with Generative Adversarial Networks

Paper content:
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.
Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks.
Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous.
We achieve state-of-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies.
Given a collection of data it is often desirable to automatically determine which instances of it are unusual.
Commonly referred to as anomaly detection, this is a fundamental machine learning task with numerous applications in fields such as astronomy BID40 BID10 , medicine BID4 BID47 BID42 , fault detection BID16 , and intrusion detection BID14 BID17 .
Traditional algorithms often focus on the low-dimensional regime and face difficulties when applied to high-dimensional data such as images or speech.
Second to that, they require the manual engineering of features.Deep learning omits manual feature engineering and has become the de-facto approach for tackling many high-dimensional machine learning tasks.
The latter is largely a testament of its experimental performance: deep learning has helped to achieve impressive results in image classification BID22 , and is setting new standards in domains such as natural language processing BID23 BID46 and speech recognition BID2 .In
this paper we present a novel deep learning based approach to anomaly detection which uses generative adversarial networks (GANs) BID15 . GANs
have achieved state-ofthe-art performance in high-dimensional generative modeling. In a
GAN, two neural networksthe discriminator and the generator -are pitted against each other. In the
process the generator learns to map random samples from a low-dimensional to a high-dimensional space, mimicking the target dataset. If the
generator has successfully learned a good approximation of the training data's distribution it is reasonable to assume that, for a sample drawn from the data distribution, there exists some point in the GAN's latent space which, after passing it through the generator network, should closely resembles this sample. We use
this correspondence to perform anomaly detection with GANs (ADGAN).In Section
2 we give an overview of previous work on anomaly detection and discuss the modeling assumptions of this paper. Section 3
contains a description of our proposed algorithm. In our experiments
, see Section 4, we both validate our method against traditional methods and showcase ADGAN's ability to detect anomalies in high-dimensional data.
We showed that searching the latent space of the generator can be leveraged for use in anomaly detection tasks.
To that end, our proposed method: (i.) delivers state-of-the-art performance on standard image benchmark datasets; (ii.) can be used to scan large collections of unlabeled images for anomalous samples.To the best of our knowledge we also reported the first results of using VAEs for anomaly detection.
We remain optimistic that boosting its performance is possible by additional tuning of the underlying neural network architecture or an informed substitution of the latent prior.Accounting for unsuitable initializations by jointly optimizing latent vectors and generator parameterization are key ingredients to help ADGAN achieve strong experimental performance.
Nonetheless, we are confident that approaches such as initializing from an approximate inversion of the generator as in ALI BID8 , or substituting the reconstruction loss for a more elaborate variant, such as the Laplacian pyramid loss BID26 , can be used to improve our method further.

assistant: text: A GAN based approach to doing anomaly detection for image data where the generator's latent space is explored to find a representation for a test image.