  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.
