arXiv:2005.10211
공식 코드 · Papers with Code
A Metric Learning Approach to Anomaly Detection in Video Games
원문 초록
With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (AD) problem. We develop State-State Siamese Networks (S3N) as an efficient deep metric learning approach to AD in this context and explore how it may be used as part of an automated testing tool. Finally, we show by empirical evaluation on a series of Atari games, that S3N is able to learn a meaningful embedding, and consequently is able to identify various common types of video game bugs.
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Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Evaluating Language Models For Threat Detection in IoT Security Logs
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series
E-ABIN: an Explainable module for Anomaly detection in BIological Networks
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