To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

The goal of image style transfer is to render an image with artistic features guided by a style
reference while maintaining the original content. Owing to the locality in convolutional neural …

Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …

Content and style (CS) disentanglement is a fundamental problem and critical challenge of
style transfer. Existing approaches based on explicit definitions (eg, Gram matrix) or implicit …

Universal style transfer retains styles from reference images in content images. While
existing methods have achieved state-of-the-art style transfer performance, they are not …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Video style transfer is attracting increasing attention from the artificial intelligence community
because of its numerous applications, such as augmented reality and animation production …

Artistic style transfer is an image editing task that aims at repainting everyday photographs
with learned artistic styles. Existing methods learn styles from either a single style example …

Recent studies have shown remarkable success in universal style transfer which transfers
arbitrary visual styles to content images. However, existing approaches suffer from the …

There have been many successful implementations of neural style transfer in recent years.
In most of these works, the stylization process is confined to the pixel domain. However, we …