Dataset distillation (DD) offers a compelling approach in computer vision,
with the goal of condensing extensive datasets into smaller synthetic versions
without sacrificing much of the model performance. In this paper, we continue
to study the methods for DD, by addressing its conceptually core objective: how
to capture the essential representation of extensive datasets in smaller,
synthetic forms.
We propose a novel approach utilizing the Wasserstein distance, a metric
rooted in optimal transport theory, to enhance distribution matching in DD. Our
method leverages the Wasserstein barycenter, offering a geometrically
meaningful way to quantify distribution differences and effectively capture the
centroid of a set of distributions. Our approach retains the computational
benefits of distribution matching-based methods while achieving new
state-of-the-art performance on several benchmarks.
To provide useful prior for learning the images, we embed the synthetic data
into the feature space of pretrained classification models to conduct
distribution matching. Extensive testing on various high-resolution datasets
confirms the effectiveness and adaptability of our method, indicating the
promising yet unexplored capabilities of Wasserstein metrics in dataset
distillation.

利用 Wasserstein 距离进行数据集简化，通过嵌入合成数据到预训练分类模型的特征空间进行分布匹配，实现了数据集简化的新的最先进性能。