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Non-Parametric Style Transfer
For this generalization, first, we propose a new feature transform layer that exactly matches the feature map distribution of content image into that of target style image.
Total Style Transfer with a Single Feed-Forward Network
To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale.
Unbiased Image Style Transfer
To solve this problem of the biased network, we propose an unbiased learning technique which uses unbiased training data and corresponding unbiased loss for alpha = 0. 0 to make the feed-forward networks to generate a zero-style image, i. e., content image when alpha = 0. 0.
Uncorrelated Feature Encoding for Faster Image Style Transfer
Although the pre-trained network is used to generate responses of receptive fields effective for representing style and content of image, it is not optimized for image style transfer but rather for image classification.
