Preprint Open access
Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation
Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite training budget. Clean and degraded twins share content and labels, suggesting a score based on how much short training reduces the excess error caused by degradation. …