ConfiG uses teacher-student confidence disagreement to guide diffusion-based augmentation and improves worst-group accuracy under unknown covariate shift in knowledge distillation.
Synthetic Data from Diffusion Models Improves ImageNet Classification
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation
ConfiG uses teacher-student confidence disagreement to guide diffusion-based augmentation and improves worst-group accuracy under unknown covariate shift in knowledge distillation.