4,756 research outputs found
Generative adversarial networks for anomaly detection in images
Anomaly detection is used to identify abnormal observations that don t follow a normal pattern. Inthis work, we use the power of Generative Adversarial Networks in sampling from image distributionsto perform anomaly detection with images and to identify local anomalous segments within thisimages. Also, we explore potential application of this method to support pathological analysis ofbiological tissue
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics
Anomaly detection is the process of finding data points that deviate from a
baseline. In a real-life setting, anomalies are usually unknown or extremely
rare. Moreover, the detection must be accomplished in a timely manner or the
risk of corrupting the system might grow exponentially. In this work, we
propose a two level framework for detecting anomalies in sequences of discrete
elements. First, we assess whether we can obtain enough information from the
statistics collected from the discriminator's layers to discriminate between
out of distribution and in distribution samples. We then build an unsupervised
anomaly detection module based on these statistics. As to augment the data and
keep track of classes of known data, we lean toward a semi-supervised
adversarial learning applied to discrete elements.Comment: 5 pages, 53rd Annual Conference on Information Sciences and Systems,
CISS 201
Anomaly Detection for imbalanced datasets with Deep Generative Models
Many important data analysis applications present with severely imbalanced
datasets with respect to the target variable. A typical example is medical
image analysis, where positive samples are scarce, while performance is
commonly estimated against the correct detection of these positive examples. We
approach this challenge by formulating the problem as anomaly detection with
generative models. We train a generative model without supervision on the
`negative' (common) datapoints and use this model to estimate the likelihood of
unseen data. A successful model allows us to detect the `positive' case as low
likelihood datapoints.
In this position paper, we present the use of state-of-the-art deep
generative models (GAN and VAE) for the estimation of a likelihood of the data.
Our results show that on the one hand both GANs and VAEs are able to separate
the `positive' and `negative' samples in the MNIST case. On the other hand, for
the NLST case, neither GANs nor VAEs were able to capture the complexity of the
data and discriminate anomalies at the level that this task requires. These
results show that even though there are a number of successes presented in the
literature for using generative models in similar applications, there remain
further challenges for broad successful implementation.Comment: 15 pages, 13 figures, accepted by Benelearn 2018 conferenc
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