Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually. Automatic data augmentation aims at automating this process. However, most recent approaches still rely on some prior information; they start from a small pool of manually-selected default transformations that are either used to pretrain the network or forced to be part of the policy learned by the automatic data augmentation algorithm. In this paper, we propose to directly learn the augmentation policy without leveraging such prior knowledge. The resulting bilevel optimization problem becomes more challenging due to the larger search space and the inherent instability of bilevel optimization algorithms. To mitigate these issues (i) we follow a successive cold-start strategy with a Kullback-Leibler regularization, and (ii) we parameterize magnitudes as continuous distributions. Our approach leads to competitive results on standard benchmarks despite a more challenging setting, and generalizes beyond natural images.

翻译：数据增强能够提升神经网络的泛化能力，但前提是需要谨慎选择变换集合——这一选择通常需人工完成。自动数据增强旨在实现该流程的自动化。然而，近期多数方法仍依赖先验信息：它们从一组手动选取的默认变换出发，这些变换或用于预训练网络，或被迫成为自动增强算法所学策略的组成部分。本文提出无需借助此类先验知识即可直接学习增强策略的新方法。由于搜索空间扩大及双层优化算法固有的不稳定性，由此产生的双层优化问题更具挑战性。为缓解这些问题，我们（i）采用基于库尔贝克-莱布勒正则化的连续冷启动策略，并（ii）将幅度参数化为连续分布。尽管面临更具挑战性的设置，我们的方法仍在标准基准测试中取得具有竞争力的结果，且其泛化能力可超越自然图像范畴。