EviDep uses evidential learning with a Normal-Inverse-Gamma distribution, wavelet-based feature extraction, and explicit feature disentanglement to deliver accurate multimodal depression estimates with calibrated uncertainty.
Deep evidential regression
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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EMSFD uses Dirichlet-based evidence modeling to capture prediction uncertainty in synthetic face detection and applies uncertainty-driven active learning to achieve 15% higher accuracy than prior methods.
citing papers explorer
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EviDep: Trustworthy Multimodal Depression Estimation via Disentangled Evidential Learning
EviDep uses evidential learning with a Normal-Inverse-Gamma distribution, wavelet-based feature extraction, and explicit feature disentanglement to deliver accurate multimodal depression estimates with calibrated uncertainty.
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Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning
EMSFD uses Dirichlet-based evidence modeling to capture prediction uncertainty in synthetic face detection and applies uncertainty-driven active learning to achieve 15% higher accuracy than prior methods.