Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
Hyperspectral Anomaly Detection Based on Spatial –Spectral Cross -Guided Mask Autoencoder,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes a prior-free anomaly detection framework for sub-canopy UAV multispectral point clouds that estimates solar angle via inverse optimization and uses illumination-consistent background dictionaries to separate targets from shadows.
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
citing papers explorer
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Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
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Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds
Proposes a prior-free anomaly detection framework for sub-canopy UAV multispectral point clouds that estimates solar angle via inverse optimization and uses illumination-consistent background dictionaries to separate targets from shadows.
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Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.