SDRS uses designed experiments and ANOVA decomposition on synthetic data to identify Type I coverage gaps and Type II spurious dependencies in vision models, then generates targeted data to improve performance.
Difficulty-controlled diffusion model for effective synthetic dataset generation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.
citing papers explorer
-
Synthetic Designed Experiments for Diagnosing Vision Model Failure
SDRS uses designed experiments and ANOVA decomposition on synthetic data to identify Type I coverage gaps and Type II spurious dependencies in vision models, then generates targeted data to improve performance.
-
LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.