What does a neural network learn when training from a task-specific dataset? Synthesizing this knowledge is the central idea behind Dataset Distillation, which recent work has shown can be used to compress large datasets into a small set of input-label pairs ($\textit{prototypes}$) that capture essential aspects of the original dataset. In this paper, we make the key observation that existing methods distilling into explicit prototypes are very often suboptimal, incurring in unexpected storage cost from distilled labels. In response, we propose $\textit{Distributional Dataset Distillation}$ (D3), which encodes the data using minimal sufficient per-class statistics and paired with a decoder, we distill dataset into a compact distributional representation that is more memory-efficient compared to prototype-based methods. To scale up the process of learning these representations, we propose $\textit{Federated distillation}$, which decomposes the dataset into subsets, distills them in parallel using sub-task experts and then re-aggregates them. We thoroughly evaluate our algorithm on a three-dimensional metric and show that our method achieves state-of-the-art results on TinyImageNet and ImageNet-1K. Specifically, we outperform the prior art by $6.9\%$ on ImageNet-1K under the storage budget of 2 images per class.

神经网络在从特定任务的数据集中进行训练时学到了什么？数据集精炼是合成这种知识的中心思想，我们提出了分布式数据集精炼（D3）方法，使用最小的充分类别统计信息，与解码器一起使数据集转化为一种更节省内存的分布式表示方法。通过对数据集进行分割、并行使用子任务专家进行精炼然后重新聚合的方式，我们提出了联邦精炼方法，以扩大学习这些表示的过程。我们对其进行全面评估，并在TinyImageNet和ImageNet-1K数据集上展示了其在三维度度量上达到了最先进结果。特别地，在每类存储预算为2张图片的情况下，我们在ImageNet-1K数据集上的结果优于先前的方法6.9%。

分布式数据集蒸馏与子任务分解