Dataset distillation synthesizes a small set of images from a large-scale real dataset such that synthetic and real images share similar behavioral properties (e.g, distributions of gradients or features) during a training process. Through extensive analyses on current methods and real datasets, together with empirical observations, we provide in this paper two important things to share for dataset distillation. First, object parts that appear on one side of a real image are highly likely to appear on the opposite side of another image within a dataset, which we call the bilateral equivalence. Second, the bilateral equivalence enforces synthetic images to duplicate discriminative parts of objects on both the left and right sides of the images, limiting the recognition of subtle differences between objects. To address this problem, we introduce a surprisingly simple yet effective technique for dataset distillation, dubbed FYI, that enables distilling rich semantics of real images into synthetic ones. To this end, FYI embeds a horizontal flipping technique into distillation processes, mitigating the influence of the bilateral equivalence, while capturing more details of objects. Experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet demonstrate that FYI can be seamlessly integrated into several state-of-the-art methods, without modifying training objectives and network architectures, and it improves the performance remarkably.

通过在当前方法和实际数据集上进行广泛分析，我们提供了两个重要的事实，即实际图像数据集中出现在一侧的目标部分很可能在另一幅图像的相反一侧出现，这被称为双边等价性；为了解决此问题，我们引入了一种名为FYI的技术，它能够将真实图像的丰富语义融入到合成图像中，并通过水平翻转技术减轻双边等价性的影响，从而捕捉更多目标的细节。

FYI：图像翻转用于数据集蒸馏