The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

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

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 …

These days deep learning is the fastest-growing field in the field of Machine Learning (ML)
and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional …

Gatys et al. recently introduced a neural algorithm that renders a content image in the style
of another image, achieving so-called style transfer. However, their framework requires a …

Universal style transfer aims to transfer arbitrary visual styles to content images. Existing
feed-forward based methods, while enjoying the inference efficiency, are mainly limited by …

Artistic text generation aims to amplify the aesthetic qualities of text while maintaining
readability. It can make the text more attractive and better convey its expression, thus …

Neural Style Transfer has recently demonstrated very exciting results which catches eyes in
both academia and industry. Despite the amazing results, the principle of neural style …

Feed-forward CNNs trained for image transformation problems rely on loss functions that
measure the similarity between the generated image and a target image. Most of the …

We propose StyleBank, which is composed of multiple convolution filter banks and each
filter bank explicitly represents one style, for neural image style transfer. To transfer an …