94 research outputs found
Divide and Conquer in Neural Style Transfer for Video
Neural Style Transfer is a class of neural algorithms designed to redraw a given image in the style of another image, traditionally a famous painting, while preserving the underlying details. Applying this process to a video requires stylizing each of its component frames, and the stylized frames must have temporal consistency between them to prevent flickering and other undesirable features. Current algorithms accommodate these constraints at the expense of speed.
We propose an algorithm called Distributed Artistic Videos and demonstrate its capacity to produce stylized videos over ten times faster than the current state-of-the-art with no reduction in output quality. Through the use of an 8-node computing cluster, we reduce the average time required to stylize a video by 92%—from hours to minutes---compared to the most recent algorithm of this kind on the same equipment and input. This allows the stylization of videos that are longer and higher-resolution than previously feasible
Demystifying Neural Style Transfer
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 transfer, especially why the Gram matrices could
represent style remains unclear. In this paper, we propose a novel
interpretation of neural style transfer by treating it as a domain adaptation
problem. Specifically, we theoretically show that matching the Gram matrices of
feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with
the second order polynomial kernel. Thus, we argue that the essence of neural
style transfer is to match the feature distributions between the style images
and the generated images. To further support our standpoint, we experiment with
several other distribution alignment methods, and achieve appealing results. We
believe this novel interpretation connects these two important research fields,
and could enlighten future researches.Comment: Accepted by IJCAI 201
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
In this paper, we introduce an unsupervised learning approach to
automatically discover, summarize, and manipulate artistic styles from large
collections of paintings. Our method is based on archetypal analysis, which is
an unsupervised learning technique akin to sparse coding with a geometric
interpretation. When applied to deep image representations from a collection of
artworks, it learns a dictionary of archetypal styles, which can be easily
visualized. After training the model, the style of a new image, which is
characterized by local statistics of deep visual features, is approximated by a
sparse convex combination of archetypes. This enables us to interpret which
archetypal styles are present in the input image, and in which proportion.
Finally, our approach allows us to manipulate the coefficients of the latent
archetypal decomposition, and achieve various special effects such as style
enhancement, transfer, and interpolation between multiple archetypes.Comment: Accepted at NIPS 2018, Montr\'eal, Canad
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