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Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
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Deep one-class classification
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Safe model-based reinforcement learning with stability guarantees
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Explaining nonlinear classification decisions with deep Taylor decomposition
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
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Unmasking clever hans predictors and assessing what machines really learn
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Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text
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Classification-based anomaly detection for general data
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Iterative energy-based projection on a normal data manifold for anomaly localization
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DROCC: Deep robust one-class classification
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Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
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Towards visually explaining variational autoencoders
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Encoding structure-texture relation with p-net for anomaly detection in retinal images
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A unifying review of deep and shallow anomaly detection
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