While recent automated data augmentation methods lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors. In this work, instead of fixing a set of hand-picked default augmentations alongside the searched data augmentations, we propose a fully automated approach for data augmentation search named Deep AutoAugment (DeepAA). DeepAA progressively builds a multi-layer data augmentation pipeline from scratch by stacking augmentation layers one at a time until reaching convergence. For each augmentation layer, the policy is optimized to maximize the cosine similarity between the gradients of the original and augmented data along the direction with low variance. Our experiments show that even without default augmentations, we can learn an augmentation policy that achieves strong performance with that of previous works. Extensive ablation studies show that the regularized gradient matching is an effective search method for data augmentation policies. Our code is available at: https://github.com/MSU-MLSys-Lab/DeepAA .

翻译：虽然最近的自动化数据增强方法导致最新的先进结果,但其设计空间和衍生的数据增强战略仍然包含强大的人类前科。 在这项工作中,我们建议采用完全自动化的方法进行数据增强搜索,名为深海自动降级(DepeAAA)。DeepAA 逐步从零到零建立多层数据增强管道,将增强层叠叠,直至达到趋同。对于每个增级层,该政策将优化,以尽量扩大原数据与扩大数据在低差异方向上的相近性。我们的实验显示,即使没有默认增强,我们也能学习出一个增强政策,与以往的工程取得很强的性能。广泛的反通货膨胀研究表明,定期的梯度匹配是数据增强政策的有效搜索方法。我们的代码可在以下网址查阅:https://github.com/MSU-MLMLSys-Lab/DepAA。