A Study in Dataset Pruning for Image Super-Resolution

arXiv:2403.17083v1 Announce Type: cross
Abstract: In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword. While offering rich training material, they also demand substantial computational and storage resources. In this work, we analyze dataset pruning as a solution to these challenges. We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model. By focusing the training on just …

abstract analyze arxiv challenges computational cs.ai cs.cv cs.gr cs.lg dataset datasets demand eess.iv image large datasets material novel pruning resolution resources solution storage study training training material type work