It is well-known that the performance of well-trained deep neural networks may degrade significantly when they are applied to data with even slightly shifted distributions. Recent studies have shown that introducing certain perturbation on feature statistics (\eg, mean and standard deviation) during training can enhance the cross-domain generalization ability. Existing methods typically conduct such perturbation by utilizing the feature statistics within a mini-batch, limiting their representation capability. Inspired by the domain generalization objective, we introduce a novel Adversarial Style Augmentation (ASA) method, which explores broader style spaces by generating more effective statistics perturbation via adversarial training. Specifically, we first search for the most sensitive direction and intensity for statistics perturbation by maximizing the task loss. By updating the model against the adversarial statistics perturbation during training, we allow the model to explore the worst-case domain and hence improve its generalization performance. To facilitate the application of ASA, we design a simple yet effective module, namely AdvStyle, which instantiates the ASA method in a plug-and-play manner. We justify the efficacy of AdvStyle on tasks of cross-domain classification and instance retrieval. It achieves higher mean accuracy and lower performance fluctuation. Especially, our method significantly outperforms its competitors on the PACS dataset under the single source generalization setting, \eg, boosting the classification accuracy from 61.2\% to 67.1\% with a ResNet50 backbone. Our code will be available at \url{https://github.com/YBZh/AdvStyle}.

翻译：众所周知,受过良好训练的深神经网络的性能在应用到甚至略有变化的分布分布数据时可能会显著下降。最近的研究显示,在培训期间对特征统计进行某些扰动(示、平均值和标准偏差),可以增强跨部概括能力。现有方法通常通过在小型批量范围内利用特征统计进行这种扰动,限制其代表性能力。受域一般化目标的启发,我们采用了一种新型的反转样式增强(ASA)方法,通过对称训练产生更有效的数据渗透空间。具体地说,我们首先通过最大限度地减少任务损失来寻找最敏感的特征统计方向和强度。通过在培训期间对对抗性统计进行更新模型,我们允许模型探索最坏的域,从而改进其总体化性能。为了便利应用ASA,我们设计了一个简单而有效的模块,即AdvStyal 代码,将ASA 方法以插入和显示精度精度的精度,OVSylorationaltality, 在Syv-chareal Servil Servial Serview 上,我们在普通的Syal-laview rolation Syal 和Sylation 。我们Adal 的高级Sylation 的效能,在Sylviewxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx