论文标题
StackMix：互补混合算法
StackMix: A complementary Mix algorithm
论文作者
论文摘要
将多个图像作为输入/输出组合的技术已被证明是培训卷积神经网络的有效数据增强。在本文中，我们介绍stackmix：每个输入都作为两个图像的串联表示，标签是两个单热标签的平均值。在“混合”工作系列中，StackMix本身可以与其他广泛使用的方法相媲美。更重要的是，与以前的工作不同，通过将StackMix与现有的混合增强相结合，有效地混合了两个以上的图像来实现各种基准的显着增长。例如，通过将StackMix与CutMix结合在一起，在各种设置中，在各种环境中的测试误差与CutMix相比有所改善，包括Imagenet上的0.8 \％，在Tiny Imagenet上为3 \％，CIFAR-100，CIFAR-100的2 \％，CIFAR-10上的0.5 \％在CIFAR-10和STL-10上为1.5 \％。混合使用也取得了类似的结果。我们进一步表明，通过将StackMix与AugMix相结合，在CIFAR-100-C上的鲁棒性稳健性，对CIFAR-100-C的稳健性和扰动有所不同。本身，使用StackMix在CIFAR-100上使用不同数量的标记样品进行改进，在测试准确性中保持了大约2 \％的间隙 - 仅使用整个数据集的5 \％ - 在半监测设置中具有2 \％的改进，并具有2 \％的改进，并具有标准的Benchmark benchmark $π$ -Model-π$ -Model。最后，我们进行了广泛的消融研究，以更好地了解所提出的算法。
Techniques combining multiple images as input/output have proven to be effective data augmentations for training convolutional neural networks. In this paper, we present StackMix: Each input is presented as a concatenation of two images, and the label is the mean of the two one-hot labels. On its own, StackMix rivals other widely used methods in the "Mix" line of work. More importantly, unlike previous work, significant gains across a variety of benchmarks are achieved by combining StackMix with existing Mix augmentation, effectively mixing more than two images. E.g., by combining StackMix with CutMix, test error in the supervised setting is improved across a variety of settings over CutMix, including 0.8\% on ImageNet, 3\% on Tiny ImageNet, 2\% on CIFAR-100, 0.5\% on CIFAR-10, and 1.5\% on STL-10. Similar results are achieved with Mixup.We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0.7\% improvement on CIFAR-100-C, by combining StackMix with AugMix over AugMix. On its own, improvements with StackMix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2\% gap in test accuracy -- down to using only 5\% of the whole dataset -- and is effective in the semi-supervised setting with a 2\% improvement with the standard benchmark $Π$-model. Finally, we perform an extensive ablation study to better understand the proposed algorithm.
