anomaly detection
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SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection and Localization
However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different
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ADGym: Design Choices for Deep Anomaly Detection
Many WSAD methods attribute their improvements to novel network architectures or loss functions, based on the authors' understanding of
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MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
However, these methods still suffer from an over-generalization issue and fail to deliver consistently high performance.
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