Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to features from more advanced and lightweight networks, such as those in the ResNet family. By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy. Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights. This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer.

研究表明，通过预训练的VGG网络提取的特征之间的相关性能够很好的捕捉图像的视觉风格；然而，这种特征传递的鲁棒性在应用于更先进的轻量级网络（如ResNet）中有所下降。本文通过大量实验发现，残差连接对于ResNet来说，生成的特征熵很小，不适合进行风格迁移；提出了一种基于softmax转换的特征激活策略，可以增强网络对于传输特征的适应性，提高各种网络类型的风格化效果。

重新思考和提高图像风格转移的鲁棒性