Data augmentation (DA) is widely used to improve the generalization of neural
networks by enforcing the invariances and symmetries to pre-defined
transformations applied to input data. However, a fixed augmentation policy may
have different effects on each sample in different training stages but existing
approaches cannot adjust the policy to be adaptive to each sample and the
training model. In this paper, we propose Model Adaptive Data Augmentation
(MADAug) that jointly trains an augmentation policy network to teach the model
when to learn what. Unlike previous work, MADAug selects augmentation operators
for each input image by a model-adaptive policy varying between training
stages, producing a data augmentation curriculum optimized for better
generalization. In MADAug, we train the policy through a bi-level optimization
scheme, which aims to minimize a validation-set loss of a model trained using
the policy-produced data augmentations. We conduct an extensive evaluation of
MADAug on multiple image classification tasks and network architectures with
thorough comparisons to existing DA approaches. MADAug outperforms or is on par
with other baselines and exhibits better fairness: it brings improvement to all
classes and more to the difficult ones. Moreover, MADAug learned policy shows
better performance when transferred to fine-grained datasets. In addition, the
auto-optimized policy in MADAug gradually introduces increasing perturbations
and naturally forms an easy-to-hard curriculum.

模型自适应数据增强（MADAug）是一种通过训练数据增强策略网络来向模型教授何时学习何事的方法，旨在优化泛化性能。MADAug 通过模型自适应策略在训练阶段选择每个输入图像的增强操作，构建逐步优化的数据增强课程。在多个图像分类任务中，MADAug 在性能上超过或与其他基线方法相媲美，并且在所有类别中都能带来改进，特别是对于难分类的类别。此外，MADAug 的策略在转移到细粒度数据集时表现更好，逐渐引入越来越大的扰动，并自然地形成一个从简单到困难的课程。