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.

通过交替优化分类器和混合样本生成器，我们提出了一种对抗性自动混合增强方法 AdAutomixup，用于生成具有挑战性的样本以训练强大的图像分类器，并通过引入指数移动平均教师和余弦相似度来防止图像内在意义的崩溃。大量实验证明，我们的方法在各种分类场景中优于现有技术水平。

对抗自动混合

Adversarial AutoMixup

With the remarkable progress of deep neural networks in computer vision, data
mixing augmentation techniques are widely studied to alleviate problems of
degraded generalization when the amount of training data is limited. However,
mixup strategies have not been well assembled in current vision toolboxes. In
this paper, we propose \texttt{OpenMixup}, an open-source all-in-one toolbox
for supervised, semi-, and self-supervised visual representation learning with
mixup. It offers an integrated model design and training platform, comprising a
rich set of prevailing network architectures and modules, a collection of data
mixing augmentation methods as well as practical model analysis tools. In
addition, we also provide standard mixup image classification benchmarks on
various datasets, which expedites practitioners to make fair comparisons among
state-of-the-art methods under the same settings. The source code and user
documents are available at https://github.com/Westlake-AI/openmixup.

本文提出了一种称之为 OpenMixup 的深度神经网络图像数据拼接工具，可以应用于监督、半监督和自监督学习，具有集成模型设计和训练平台、各种数据混合增强方法以及实用的模型分析工具等功能。同时还在多个数据集上提供图像分类基准测试，以加速研究人员在同一设置下进行公平比较。