Improving the Neural Algorithm of Artistic Style

By Roman Novak, Yaroslav Nikulin. 2016

33 citations recorded by the source corpus.

In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style (by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge, arXiv:1508.06576). While showing great results when transferring homogeneous and repetitive patterns, the original style representation often fails to capture more complex properties, like having separate styles of foreground and background. This leads to visual artifacts and undesirable textures appearing in unexpected regions when performing style transfer. We tackle this issue with a variety of approaches, mostly by modifying the style representation in order for it to capture more information and impose a tighter constraint on the style transfer result. In our experiments, we subjectively evaluate our best method as producing from barely noticeable to significant improvements in the quality of style transfer.

Subfields: Cognitive Neuroscience, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Style transfer-based image synthesis as an efficient regularization technique in deep learning (2019, methodology): “To synthesize the images we used the method of artistic neural style transfer based on [11] with improvements detailed in [40], implemented in Keras 2.”
Data augmentation for improving deep learning in image classification problem (2018, methodology): “with improvements detailed in [42], implemented in Keras 2.”
Neural Style Transfer: A Review (2017, background): “[34, 35] Slight Modifications by varying experimental settings, etc.”