论文标题
递归内容：混合学习与历史
RecursiveMix: Mixed Learning with History
论文作者
论文摘要
基于混合的增强已被证明是深视觉模型的概括。但是，当前的增强仅在培训期间在当前数据批次上混合样本，这忽略了学习史中可能的知识。在本文中，我们提出了一种递归的混合样本学习范式，称为“递归”（RM），通过探索一种利用历史输入预测标签三重态的新型培训策略。更具体地说，我们从上一个迭代中迭代调整输入映像批次大小，并将其粘贴到当前批次中，而它们的标签则与操作的补丁区域成比例地融合在一起。此外，引入一致性损失是为了使整个迭代中相同的图像语义对齐，这有助于学习规模不变特征表示。基于Resnet-50，RM在CIFAR100上的分类准确性在很大程度上提高了$ \ sim $ 3.2 \％，而Imagenet上的Imagenet上的分类准确性则可以提高$ \ sim $ 2.8 \％。在下游对象检测任务中，RM预处理的模型的表现优于基线2.1 AP点，并在可可座的ATSS检测器下超过1.4 AP点。在语义分段中，RM还分别超过了基线和cutmix，分别在ADE20K上的Upernet下方的upernet下方1.9和1.1 miOU点。 \ url {https://github.com/megvii-research/recursivemix}可用代码和预估计的模型。
Mix-based augmentation has been proven fundamental to the generalization of deep vision models. However, current augmentations only mix samples at the current data batch during training, which ignores the possible knowledge accumulated in the learning history. In this paper, we propose a recursive mixed-sample learning paradigm, termed "RecursiveMix" (RM), by exploring a novel training strategy that leverages the historical input-prediction-label triplets. More specifically, we iteratively resize the input image batch from the previous iteration and paste it into the current batch while their labels are fused proportionally to the area of the operated patches. Further, a consistency loss is introduced to align the identical image semantics across the iterations, which helps the learning of scale-invariant feature representations. Based on ResNet-50, RM largely improves classification accuracy by $\sim$3.2\% on CIFAR100 and $\sim$2.8\% on ImageNet with negligible extra computation/storage costs. In the downstream object detection task, the RM pretrained model outperforms the baseline by 2.1 AP points and surpasses CutMix by 1.4 AP points under the ATSS detector on COCO. In semantic segmentation, RM also surpasses the baseline and CutMix by 1.9 and 1.1 mIoU points under UperNet on ADE20K, respectively. Codes and pretrained models are available at \url{https://github.com/megvii-research/RecursiveMix}.
