Neural Batch Sampling with Reinforcement Learning for Semi-supervised Anomaly Detection

Wen-Hsuan Chu; Kris Kitani

doi:10.1007/978-3-030-58574-7_45

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Neural Batch Sampling with Reinforcement Learning for Semi-supervised Anomaly Detection

Wen-Hsuan Chu Kris Kitani

Year: 2020 Lecture notes in computer science Pages: 751-766 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-58574-7_45

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Keywords:

Computer science Anomaly detection Artificial intelligence Pattern recognition (psychology) Segmentation Autoencoder Reinforcement learning Perceptron Generative model Classifier (UML) Anomaly (physics) Supervised learning Machine learning Artificial neural network Generative grammar

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Adversarial Robustness in Machine Learning

Physical Sciences → Computer Science → Artificial Intelligence

Neural Batch Sampling with Reinforcement Learning for Semi-supervised Anomaly Detection

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