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软标签修剪和量化用于大规模数据集蒸
IEEE transactions on pattern analysis and machine intelligence
|February 13, 2026
概括
数据集蒸面临由于大型软标签的储存挑战. 我们的LPQLD方法显著减少了标签大小,并提高了像ImageNet.Net这样的大规模数据集的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 数据压缩数据压缩
背景情况:
- 大规模数据集的蒸需要大量存储辅助软标签,通常比凝结图像大几百倍.
- 现有的方法受到图像多样性和监控多样性不足的阻碍,导致高压缩率的性能下降.
研究的目的:
- 解决大规模数据集蒸中的存储和性能问题.
- 提出一种新的方法,大规模蒸的标签修剪和量化 (LPQLD),以实现高效的数据集压缩.
主要方法:
- 在合成数据生成过程中通过分类分批和批量规范化 (BN) 监督来增强图像多样性.
- 通过动态知识再利用的标签修剪和标签量化,通过校准的学生-教师对齐来提高监督多样性.
主要成果:
- 软标签存储在ImageNet-1K上减少了78倍,在ImageNet-21K上减少了500倍.
- 在ImageNet-1K上实现了高达7.2%的精度改进,在ImageNet-21K上达到2.8%.
- 在各种网络架构中表现出优越性,与其他蒸方法相比.
结论:
- 在数据集蒸中,LPQLD有效地克服了大型软标签存储的局限性.
- 拟议的方法实现了显著的压缩比,同时提高了模型的准确性.
- LPQLD代表了高效大规模数据集蒸的卓越方法.
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