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Found 4 papers, 2 papers with code
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets.
Label-Free Multivariate Time Series Anomaly Detection
In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow.
Detecting Multivariate Time Series Anomalies with Zero Known Label
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.
Few-shot learning with improved local representations via bias rectify module
Recent approaches based on metric learning have achieved great progress in few-shot learning.
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