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.

翻译：在图像超分辨率（SR）中，依赖大型数据集进行训练是一把双刃剑。虽然大型数据集提供了丰富的训练材料，但也需要大量的计算和存储资源。在本文中，我们分析数据集修剪作为应对这些挑战的解决方案。我们引入了一种新颖方法，将数据集缩减为训练样本的核心子集，这些样本基于由简单预训练SR模型确定的损失值进行选择。通过仅使用原始数据集的50%进行训练，特别是选择具有最高损失值的样本，我们实现了与在整个数据集上训练相当甚至更优的结果。有趣的是，我们的分析表明，损失值最高的前5%样本会对训练过程产生负面影响。排除这些样本，并调整选择以偏好更简单的样本，进一步提升了训练效果。我们的工作为图像SR中数据集修剪的未开发潜力开辟了新视角，表明基于损失值度量的训练数据精心选择可以生成更好的SR模型，挑战了“更多数据必然带来更好性能”的传统观念。