Image Style Transfer and Content-Style Disentanglement
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Abstract (arXiv)
We propose a way of learning disentangled content-style representation of image, allowing us to extrapolate images to any style as well as interpolate between any pair of styles. By augmenting data set in a supervised setting and imposing triplet loss, we ensure the separation of information encoded by content and style representation. We also make use of cycle-consistency loss to guarantee that images could be reconstructed faithfully by their representation.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 25 Nov 2021.
Builds on
15 of 20 references.
References · 15
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- 2017Exploring the structure of a real-time, arbitrary neural artistic stylization networkcited by 89
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Citing papers per year: 2022: 1.
1 paper.