Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
CVPR 2024(2023)
摘要
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
128-width ConvNet, 47.6
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
and 10.1
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要