We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both classification and regression based tasks. During training, our style augmentation randomizes texture, contrast and color, while preserving shape and semantic content. This is accomplished by adapting an arbitrary style transfer network to perform style randomization, by sampling input style embeddings from a multivariate normal distribution instead of inferring them from a style image. In addition to standard classification experiments, we investigate the effect of style augmentation (and data augmentation generally) on domain transfer tasks. We find that data augmentation significantly improves robustness to domain shift, and can be used as a simple, domain agnostic alternative to domain adaptation. Comparing style augmentation against a mix of seven traditional augmentation techniques, we find that it can be readily combined with them to improve network performance. We validate the efficacy of our technique with domain transfer experiments in classification and monocular depth estimation, illustrating consistent improvements in generalization.

本文提出了一种基于随机样式转换的数据增广方法，即样式增广，以提高卷积神经网络在分类、回归和领域迁移任务中的鲁棒性。在训练期间，通过从多元正态分布中采样输入样式嵌入，将任意样式转换网络调整为执行样式随机化，以随机化纹理、对比度和颜色，同时保留形状和语义内容，并且与传统的数据增广技术相结合以改进网络性能。通过分类和单目深度估计领域迁移实验的验证，证明本文提出的技术方法能够提高模型的泛化能力。

样式增强: 通过样式随机化进行数据增强