ContrastAD achieves highest mean F1 on all five MTS benchmarks and highest AUC on three by building DTW-based sparse graph snapshots and contrasting divergent pairs with a stable anchor instead of enforcing invariance.
On mutual information in contrastive learning for visual representations
2 Pith papers cite this work. Polarity classification is still indexing.
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The capacity of distinguishable synthetic identity generation under face verification is characterized by spherical-code problems on the unit hypersphere, with lower bounds derived for both deterministic and stochastic generation models.
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Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
ContrastAD achieves highest mean F1 on all five MTS benchmarks and highest AUC on three by building DTW-based sparse graph snapshots and contrasting divergent pairs with a stable anchor instead of enforcing invariance.
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On the Capacity of Distinguishable Synthetic Identity Generation under Face Verification
The capacity of distinguishable synthetic identity generation under face verification is characterized by spherical-code problems on the unit hypersphere, with lower bounds derived for both deterministic and stochastic generation models.