The high costs of annotating large datasets suggests a need for effectively training CNNs with limited data, and data augmentation is a promising direction. We study foundational augmentation techniques, including Mixed Sample Data Augmentations (MSDAs) and a no-parameter variant of RandAugment termed Preset-RandAugment, in the fully supervised scenario. We observe that Preset-RandAugment excels in limited-data contexts while MSDAs are moderately effective. We show that low-level feature transforms play a pivotal role in this performance difference, postulate a new property of augmentations related to their data efficiency, and propose new ways to measure the diversity and realism of augmentations. Building on these insights, we introduce a novel augmentation technique called RandMSAugment that integrates complementary strengths of existing methods. RandMSAugment significantly outperforms the competition on CIFAR-100, STL-10, and Tiny-Imagenet. With very small training sets (4, 25, 100 samples/class), RandMSAugment achieves compelling performance gains between 4.1% and 6.75%. Even with more training data (500 samples/class) we improve performance by 1.03% to 2.47%. RandMSAugment does not require hyperparameter tuning, extra validation data, or cumbersome optimizations.

数据增强是一种有效训练深度卷积神经网络以限制数据的方法，本研究探讨了基础增强技术、如混合样本数据增强和无参数的RandAugment变体Preset-RandAugment，在完全监督的情境下证明了Preset-RandAugment在有限数据情况下的出色表现，通过引入一种名为RandMSAugment的新型数据增强技术，有效结合了现有方法的优势，在CIFAR-100、STL-10和Tiny-Imagenet数据集上取得了显著的性能提升，无需超参数调整和繁琐的优化过程。

RandMSAugment：用于有限数据场景的混合采样增强