Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

CVPR 2017 Dmitry UlyanovAndrea VedaldiVictor Lempitsky

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper