Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training. Most existing work focuses on constructing a unified policy applicable to all data samples in a given dataset, without considering sample or class variations. In this paper, we propose a novel two-stage data augmentation algorithm, named Label-Aware AutoAugment (LA3), which takes advantage of the label information, and learns augmentation policies separately for samples of different labels. LA3 consists of two learning stages, where in the first stage, individual augmentation methods are evaluated and ranked for each label via Bayesian Optimization aided by a neural predictor, which allows us to identify effective augmentation techniques for each label under a low search cost. And in the second stage, a composite augmentation policy is constructed out of a selection of effective as well as complementary augmentations, which produces significant performance boost and can be easily deployed in typical model training. Extensive experiments demonstrate that LA3 achieves excellent performance matching or surpassing existing methods on CIFAR-10 and CIFAR-100, and achieves a new state-of-the-art ImageNet accuracy of 79.97% on ResNet-50 among auto-augmentation methods, while maintaining a low computational cost.

翻译：自动增强是一种新兴且有效的技术，用于搜索数据增强策略，以提升深度神经网络训练的泛化能力。现有工作大多集中于为给定数据集中的所有数据样本构建统一策略，而未考虑样本或类别的差异。本文提出一种新颖的两阶段数据增强算法，名为标签感知自动增强（LA3），该算法利用标签信息，针对不同标签的样本分别学习增强策略。LA3包含两个学习阶段：第一阶段通过贝叶斯优化结合神经预测器，评估并排序各标签对应的单个增强方法，从而以较低搜索成本识别针对每个标签的有效增强技术；第二阶段则从选择的有效且互补的增强方法中构建复合增强策略，该策略能显著提升性能，并可轻松部署于典型模型训练中。大量实验表明，LA3在CIFAR-10和CIFAR-100上取得了匹配或超越现有方法的优异性能，并在自动增强方法中，以ResNet-50架构在ImageNet上达到了79.97%的最新准确率，同时保持了较低的计算成本。