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MDGAN: Boosting Anomaly Detection Using Multi-Discriminator Generative Adversarial Networks
Anomaly detection is often considered a challenging field of machine lea...
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Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
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Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection
Anomaly detection in time series has been widely researched and has impo...
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Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
Deep autoencoder has been extensively used for anomaly detection. Traini...
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Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
A popular method for anomaly detection is to use the generator of an adv...
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Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
This paper proposes a new defense against neural network backdooring att...
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Unsupervised Anomaly Detection From Semantic Similarity Scores
In this paper, we present SemSAD, a simple and generic framework for det...
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A Lipschitz-constrained anomaly discriminator framework
Anomaly detection is a problem of great interest in medicine, finance, and other fields where error and fraud need to be detected and corrected. Most deep anomaly detection methods rely on autoencoder reconstruction error. However, we show that this approach has limited value. First, this approach starts to perform poorly when either noise or anomalies contaminate training data, even to a small extent. Second, this approach cannot detect anomalous but simple to reconstruct points. This can be seen even in relatively simple examples, such as feeding a black image to detectors trained on MNIST digits. Here, we introduce a new discriminator-based unsupervised Lipschitz anomaly detector (LAD). We train a Wasserstein discriminator, similar to the ones used in GANs, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. Finally, we show results of this system in an electronic medical record dataset of HIV-positive veterans from the veterans aging cohort study (VACS) to establish usability in a medical setting.
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