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MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection
Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and stability of working devices (e. g., water treatment system and spacecraft), whose data are characterized by multivariate time series with diverse modalities.
Effective Decision Boundary Learning for Class Incremental Learning
Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory.
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