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Found 6 papers, 3 papers with code
Iterative Feature Transformation for Fast and Versatile Universal Style Transfer
The general framework for fast universal style transfer consists of an autoencoder and a feature transformation at the bottleneck.
Line Search-Based Feature Transformation for Fast, Stable, and Tunable Content-Style Control in Photorealistic Style Transfer
Photorealistic style transfer is the task of synthesizing a realistic-looking image when adapting the content from one image to appear in the style of another image.
PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models
To our knowledge, this is the first knowledge distillation method for photorealistic style transfer.
PhotoWCT$^2$: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals
First, we introduce blockwise training to perform coarse-to-fine feature transformations that enable state-of-art stylization strength in a single autoencoder in place of the inefficient cascade of four autoencoders used in PhotoWCT.
Assessing Image Quality Issues for Real-World Problems
We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering.
Understanding Generalized Whitening and Coloring Transform for Universal Style Transfer
Style transfer is a task of rendering images in the styles of other images.
