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 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.
Abstract (translated)
在图像超分辨率（SR）中，依靠大量数据进行训练是一个双刃剑。虽然它们提供了丰富的训练材料，但它们也需要大量的计算和存储资源。在这项工作中，我们分析了数据集修剪作为一个解决这些挑战的解决方案。我们引入了一种新颖的方法，将数据集修剪为一个基于其损失值确定的核心集。通过将训练重点放在原始数据集的50%上，特别关注损失值最高的样本，我们实现了与或甚至超过整个数据集训练获得的结果 comparable或 even surpassing those obtained from training on the entire dataset。有趣的是，我们的分析揭示了最高损失值的前5%的样本负
URL
https://arxiv.org/abs/2403.17083
