Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier while the target classifier`s aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on seven image benchmarks consistently prove that our approach outperforms the state of the art in various classification scenarios.

翻译：数据混合增强已被广泛应用于提升深度神经网络的泛化能力。近年来，基于手工设计及显著性信息的离线数据混合增强方法正逐步被自动化混合方法取代。通过以端到端方式联合优化混合样本生成与混合分类两个子任务，AutoMix方法显著提升了图像分类任务的准确率。然而，由于两个子任务的优化目标一致，该方法倾向于生成一致而非多样化的混合样本，导致目标任务训练出现过拟合现象。本文提出对抗性自动混合增强方法AdAutomixup，通过交替优化分类器与混合样本生成器，生成具有挑战性的样本来训练鲁棒图像分类器。AdAutomixup包含混合样本生成与目标分类两个模块：混合样本生成器旨在生成困难混合样本来挑战目标分类器，而目标分类器则从困难混合样本中学习鲁棒特征以提升泛化能力。为避免图像固有语义崩塌，我们进一步引入指数移动平均教师模型与余弦相似度，以端到端方式训练AdAutomixup。在七个图像基准数据集上的大量实验一致表明，该方法在多种分类场景中均优于现有最优方法。