A UCB-based training procedure switches between a diversity/photorealism score and a feature-cohesion score to select synthetic training images, reporting up to 10-point accuracy gains over static metrics.
International Journal of Computer Vision, 1–25 (2024)
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.LG 1years
2024 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data
A UCB-based training procedure switches between a diversity/photorealism score and a feature-cohesion score to select synthetic training images, reporting up to 10-point accuracy gains over static metrics.