Non-Parametric Style Transfer

• URL: http://arxiv.org/abs/2206.12921v1
• Date: Sun, 26 Jun 2022 16:34:37 GMT
• Title: Non-Parametric Style Transfer
• Authors: Jeong-Sik Lee, Hyun-Chul Choi
• Abstract summary: Recent feed-forward neural methods of arbitrary image style transfer mainly utilized encoded feature map upto its second-order statistics. We extend the second-order statistical feature matching into a general distribution matching based on the understanding that style of an image is represented by the distribution of responses from receptive fields. Based on our results, it is proven that the stylized images obtained with our method are more similar with the target style images in all existing style measures without losing content clearness.
• Score: 0.9137554315375919
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent feed-forward neural methods of arbitrary image style transfer mainly utilized encoded feature map upto its second-order statistics, i.e., linearly transformed the encoded feature map of a content image to have the same mean and variance (or covariance) of a target style feature map. In this work, we extend the second-order statistical feature matching into a general distribution matching based on the understanding that style of an image is represented by the distribution of responses from receptive fields. 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. Second, we analyze the recent style losses consistent with our new feature transform layer to train a decoder network which generates a style transferred image from the transformed feature map. Based on our experimental results, it is proven that the stylized images obtained with our method are more similar with the target style images in all existing style measures without losing content clearness.

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