In computer vision, it is standard practice to draw a single sample from the data augmentation procedure for each unique image in the mini-batch, however it is not clear whether this choice is optimal for generalization. In this work, we provide a detailed empirical evaluation of how the number of augmentation samples per unique image influences performance on held out data. Remarkably, we find that drawing multiple samples per image consistently enhances the test accuracy achieved for both small and large batch training, despite reducing the number of unique training examples in each mini-batch. This benefit arises even when different augmentation multiplicities perform the same number of parameter updates and gradient evaluations. Our results suggest that, although the variance in the gradient estimate arising from subsampling the dataset has an implicit regularization benefit, the variance which arises from the data augmentation process harms test accuracy. By applying augmentation multiplicity to the recently proposed NFNet model family, we achieve a new ImageNet state of the art of 86.8$\%$ top-1 w/o extra data.

本文通过实验证明，在训练深层ResNets时，每个独特图像的增强样本数量如何影响模型性能，结果表明，绘制多个样本可实现更高的测试准确性，且即使每个图像的增强数目表现相同，使用这种方法也能提高测试准确度。

训练期间在图像上绘制多个变换样本可有效降低测试误差