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# Style Transfer Edit

63 papers with code · Computer Vision

Style transfer is the task of changing the style of an image in one domain to the style of an image in another domain.

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# Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. Our goal is to learn a mapping $G: X \rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss.

# A Neural Algorithm of Artistic Style

26 Aug 2015jcjohnson/neural-style

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities.

# Deep Photo Style Transfer

This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network.

# Instance Normalization: The Missing Ingredient for Fast Stylization

27 Jul 2016lengstrom/fast-style-transfer

It this paper we revisit the fast stylization method introduced in Ulyanov et. We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images.

# Preserving Color in Neural Artistic Style Transfer

19 Jun 2016cysmith/neural-style-tf

This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.). The original algorithm transforms an image to have the style of another given image.

# Artistic style transfer for videos

28 Apr 2016cysmith/neural-style-tf

Nowadays computers provide new possibilities. We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence.

# Perceptual Losses for Real-Time Style Transfer and Super-Resolution

27 Mar 2016DmitryUlyanov/texture_nets

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \emph{per-pixel} loss between the output and ground-truth images.

# Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

10 Mar 2016DmitryUlyanov/texture_nets

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process.

# Neural Style Transfer: A Review

11 May 2017ycjing/Neural-Style-Transfer-Papers

We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively.

# Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

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 slow iterative optimization process, which limits its practical application.