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Improved autoencoder for unsupervised anomaly detection
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
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Principal component analysis
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Advances in neural information processing systems 7
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Gradient-based learning applied to document recognition
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Unsupervised One-Class Learning for Automatic Outlier Removal
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Learning Discriminative Reconstructions for Unsupervised Outlier Removal
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Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection
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Robust principal component analysis?
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Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
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Anomaly Detection with Robust Deep Autoencoders
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Estimating the Support of a High-Dimensional Distribution
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Deep Anomaly Detection Using Geometric Transformations
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Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Multimethodischer KI-Ansatz für eine ultraschallbasierte Zustandsüberwachung in Echtzeit
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