9,886 research outputs found
A Neural Algorithm of Artistic 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.
However, in other key areas of visual perception such as object and face
recognition near-human performance was recently demonstrated by a class of
biologically inspired vision models called Deep Neural Networks. Here we
introduce an artificial system based on a Deep Neural Network that creates
artistic images of high perceptual quality. The system uses neural
representations to separate and recombine content and style of arbitrary
images, providing a neural algorithm for the creation of artistic images.
Moreover, in light of the striking similarities between performance-optimised
artificial neural networks and biological vision, our work offers a path
forward to an algorithmic understanding of how humans create and perceive
artistic imagery
An Exploration of Style Transfer Using Deep Neural Networks
Convolutional Neural Networks and Graphics Processing Units have been at the core of a paradigm shift in computer vision research that some researchers have called `\u27the algorithmic perception revolution. This thesis presents the implementation and analysis of several techniques for performing artistic style transfer using a Convolutional Neural Network architecture trained for large-scale image recognition tasks. We present an implementation of an existing algorithm for artistic style transfer in images and video. The neural algorithm separates and recombines the style and content of arbitrary images. Additionally, we present an extension of the algorithm to perform weighted artistic style transfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference
(BMVC) 201
Texture Modelling Using Convolutional Neural Networks
We introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. Extending this framework to texture transfer, we introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new artistic imagery that combines the content of an arbitrary photograph with the appearance of numerous well-known artworks, thus offering a path towards an algorithmic understanding of how humans create and perceive artistic imagery
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer
An unprecedented booming has been witnessed in the research area of artistic
style transfer ever since Gatys et al. introduced the neural method. One of the
remaining challenges is to balance a trade-off among three critical
aspects---speed, flexibility, and quality: (i) the vanilla optimization-based
algorithm produces impressive results for arbitrary styles, but is
unsatisfyingly slow due to its iterative nature, (ii) the fast approximation
methods based on feed-forward neural networks generate satisfactory artistic
effects but bound to only a limited number of styles, and (iii)
feature-matching methods like AdaIN achieve arbitrary style transfer in a
real-time manner but at a cost of the compromised quality. We find it
considerably difficult to balance the trade-off well merely using a single
feed-forward step and ask, instead, whether there exists an algorithm that
could adapt quickly to any style, while the adapted model maintains high
efficiency and good image quality. Motivated by this idea, we propose a novel
method, coined MetaStyle, which formulates the neural style transfer as a
bilevel optimization problem and combines learning with only a few
post-processing update steps to adapt to a fast approximation model with
satisfying artistic effects, comparable to the optimization-based methods for
an arbitrary style. The qualitative and quantitative analysis in the
experiments demonstrates that the proposed approach achieves high-quality
arbitrary artistic style transfer effectively, with a good trade-off among
speed, flexibility, and quality.Comment: AAAI 2019 spotlight. Supplementary:
http://wellyzhang.github.io/attach/aaai19zhang_supp.pdf GitHub:
https://github.com/WellyZhang/MetaStyle Project:
http://wellyzhang.github.io/project/metastyle.htm
Using Principal Paths to Walk Through Music and Visual Art Style Spaces Induced by Convolutional Neural Networks
AbstractComputational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional neural networks can capture historical/stylistic progressions in music and visual art. We use a path-finding algorithm, called principal path, to move from one point to another. We apply it to the vector space induced by convolutional neural networks. We perform experiments with visual artworks and songs, considering a subset of classes. Within this simplified scenario, we recover a reasonable historical/stylistic progression in several cases. We use the principal path algorithm to conduct an evolutionary analysis of vector spaces induced by convolutional neural networks. We perform several experiments in the visual art and music spaces. The principal path algorithm finds reasonable connections between visual artworks and songs from different styles/genres with respect to the historical evolution when a subset of classes is considered. This approach could be used in many areas to extract evolutionary information from an arbitrary high-dimensional space and deliver interesting cognitive insights
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