Learn More
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(More)
Here 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(More)
Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from(More)
Here we demonstrate that the feature space of random shallow convolutional neural networks (CNNs) can serve as a surprisingly good model of natural textures. Patches from the same texture are consistently classified as being more similar then patches from different textures. Samples synthesized from the model capture spatial correlations on scales much(More)
(a) Content (b) Style (c) Output using [2] (d) Output with color preservation (color transfer) (e) Output with colour preservation (luminance matching) Figure 1: Example using a style dominated by brushstrokes. (a) Input photograph. (b) Painting Starry night over the Rhone by Vincent van Gogh. (c) Transformed content image, using original neural style(More)
(a) Content (b) Spatial Control (c) Colour Control (d) Scale Control Figure 1: Overview of our control methods. (a) Content image, with spatial mask inset. (b) Spatial Control. The sky is stylised using the sky of Style II from Fig. 2(c). The ground is stylised using Style I from Fig. 4(b). (c) Colour Control. The colour of the content image is preserved(More)
Here we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies.
Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly(More)
Aim The general premise of our project is to create a photo booth application with the ability to use a pre­set filter to render images in the style of well­known artists. By using convolutional neural networks, we can extract high­level feature data from user images and low­level features of artistic paintings to serve as the constraints of the(More)
  • 1