多样品混合:更丰富,更现实的合成样本来自p系列的插入剂.
Kumar Abhishek1, Colin J Brown2, Ghassan Hamarneh1
1School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6 Canada.
概括
泽塔混合,一种新的数据增强技术,通过允许多个数据点的组合,产生比传统混合更现实的和多样化的合成样本. 这种方法提高了模型的概括性,并且在图像分类任务中优于现有的技术.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 数据增强对于深度学习模型规范化至关重要.
- 混合,一种流行的方法,通过凸形组合生成合成数据,但可以创建具有不正确标签的不现实的样本.
- 现有的方法与非多元数据生成作斗争.
研究的目的:
- 介绍Zeta-mixup,一种通用混合技术,用于改进数据增强.
- 为了解决现有的混合方法的局限性,例如离散式采样和不正确的标签.
- 提高合成训练数据的现实性,多样性和标签准确性.
主要方法:
- 提出Zeta-mixup,一种混合的概括,允许使用p系列插曲剂对多个样本进行凸形组合.
- 调查数据内在维度的保存情况.
- 与基线方法相比,实施和评估Zeta-mixup.
主要成果:
- 与混合相比,泽塔混合产生了更现实的和多样化的输出.
- 该方法更好地保留了数据集的内在维度,这对于可概括模型至关重要.
- 泽塔混合实现比混合更快,在26个不同的图像数据集上超过混合,CutMix和传统增强.
结论:
- 泽塔混合提供了深度学习中数据增强的可证明的理想特性.
- 该技术增强了模型的概括性和图像分类中的性能.
- 泽塔混合物对现有的基于混合物的增强策略来说是一个显著的进步.
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