论文标题
苏打水：深神经网络中的自组织数据增强 - 生物医学图像分割任务的应用
SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks
论文作者
论文摘要
实际上，根据每个时期新创建的样本，将数据扩展分配为预定义的预算。当使用几种类型的数据增强时，预算通常在一组增强板上均匀分配，但是人们会怀疑是否不应以更有效的方式将该预算分配给每种类型。本文利用在线学习可以作为神经网络培训的一部分即时分配这笔预算。该元算法可以以几乎没有额外的费用运行，因为它利用基于梯度的信号来确定应优选哪种类型的数据增强。实验表明，这种策略可以节省计算时间，因此可以避免使用更绿色的机器学习实践。
In practice, data augmentation is assigned a predefined budget in terms of newly created samples per epoch. When using several types of data augmentation, the budget is usually uniformly distributed over the set of augmentations but one can wonder if this budget should not be allocated to each type in a more efficient way. This paper leverages online learning to allocate on the fly this budget as part of neural network training. This meta-algorithm can be run at almost no extra cost as it exploits gradient based signals to determine which type of data augmentation should be preferred. Experiments suggest that this strategy can save computation time and thus goes in the way of greener machine learning practices.
