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The recent work of Gatys et al., who characterized the style of an image bythe statistics of convolutional neural network filters, ignited a renewedinterest in the texture generation and image stylization problems. While theirimage generation technique uses a slow optimization process, recently severalauthors have proposed to learn generator neural networks that can producesimilar outputs in one quick forward pass. While generator networks arepromising, they are still inferior in visual quality and diversity compared togeneration-by-optimization. In this work, we advance them in two significantways. First, we introduce an instance normalization module to replace batchnormalization with significant improvements to the quality of imagestylization. Second, we improve diversity by introducing a new learningformulation that encourages generators to sample unbiasedly from the Julesztexture ensemble, which is the equivalence class of all images characterized bycertain filter responses. Together, these two improvements take feed forwardtexture synthesis and image stylization much closer to the quality ofgeneration-via-optimization, while retaining the speed advantage.

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