POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection
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Multimodal Diffusion Forcing trains a diffusion model on partially masked multimodal robot trajectories to learn temporal and cross-modal dependencies for forceful manipulation.
U²AD learns unified normal data representations via score-based generative modeling and a novel time-dependent score network to outperform prior methods in accuracy and early anomaly detection for multivariate time series.
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
TPA-AD generates boundary-near pseudo-anomalies via reconstruction, applies contrastive learning, and uses KNN to score anomalies in bearing time series with only normal training samples.
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Multimodal Diffusion Forcing for Forceful Manipulation
Multimodal Diffusion Forcing trains a diffusion model on partially masked multimodal robot trajectories to learn temporal and cross-modal dependencies for forceful manipulation.