PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data.
Discovering cluster-based local outliers
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
citing papers explorer
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PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data.
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On Diffusion Modeling for Anomaly Detection
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.