The lightweight "local-match-global" matching introduced by SRe2L successfully creates a distilled dataset with comprehensive information on the full 224x224 ImageNet-1k. However, this one-sided approach is limited to a particular backbone, layer, and statistics, which limits the improvement of the generalization of a distilled dataset. We suggest that sufficient and various "local-match-global" matching are more precise and effective than a single one and has the ability to create a distilled dataset with richer information and better generalization. We call this perspective "generalized matching" and propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this work, which aims to create a synthetic dataset with densities, ensuring consistency with the complete dataset across various backbones, layers, and statistics. As experimentally demonstrated, G-VBSM is the first algorithm to obtain strong performance across both small-scale and large-scale datasets. Specifically, G-VBSM achieves a performance of 38.7% on CIFAR-100 with 128-width ConvNet, 47.6% on Tiny-ImageNet with ResNet18, and 31.4% on the full 224x224 ImageNet-1k with ResNet18, under images per class (IPC) 10, 50, and 10, respectively. These results surpass all SOTA methods by margins of 3.9%, 6.5%, and 10.1%, respectively.

翻译：SRe2L引入的轻量级"局部-全局匹配"策略成功构建了包含完整224x224 ImageNet-1k信息的精简数据集。然而这种单一方法受限于特定骨干网络、层级与统计量，限制了精简数据集泛化性能的提升。我们提出充分的多样化"局部-全局匹配"策略比单一匹配更精准高效，能够构建信息更丰富、泛化能力更强的精简数据集。我们将该视角称为"广义匹配"，并在本工作中提出广义多样化骨干网络与统计匹配方法（G-VBSM），旨在生成具有密度特性的合成数据集，确保其与完整数据集在多个骨干网络、层级及统计量上保持一致性。实验证明，G-VBSM是首个在中小规模及大规模数据集上均表现优异的算法。具体而言，G-VBSM在每类图像数（IPC）分别为10、50、10的条件下，使用128宽度ConvNet在CIFAR-100上达到38.7%的准确率，使用ResNet18在Tiny-ImageNet上达到47.6%，在完整224x224 ImageNet-1k上达到31.4%。这些结果分别以3.9%、6.5%和10.1%的优势超越所有现有最优方法。