A Study in Dataset Pruning for Image Super-Resolution
Brian B. Moser Federico Raue Andreas Dengel
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 50% of the original dataset, specifically on the
samples characterized by the highest loss values, we achieve results comparable
to or even surpassing those obtained from training on the entire dataset.
Interestingly, our analysis reveals that the top 5% of samples with the highest
loss values negatively affect the training process. Excluding these samples and
adjusting the selection to favor easier samples further enhances training
outcomes. Our work opens new perspectives to the untapped potential of dataset
pruning in image SR. It suggests that careful selection of training data based
on loss-value metrics can lead to better SR models, challenging the
conventional wisdom that more data inevitably leads to better performance.