Approximate subject-level unlearning recovers 89.3% and 92.5% of oracle performance gains on EngageNet and DAiSEE at roughly one-quarter the retraining cost in K=3 forget-set regimes.
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2 Pith papers cite this work. Polarity classification is still indexing.
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PriorNet improves engagement estimation from face videos by injecting priors into preprocessing, adaptation, and objective design, showing improvements on multiple benchmarks.
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Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition
Approximate subject-level unlearning recovers 89.3% and 92.5% of oracle performance gains on EngageNet and DAiSEE at roughly one-quarter the retraining cost in K=3 forget-set regimes.
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PriorNet: Prior-Guided Engagement Estimation from Face Video
PriorNet improves engagement estimation from face videos by injecting priors into preprocessing, adaptation, and objective design, showing improvements on multiple benchmarks.