Abstract | ||
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This paper introduces a neural style transfer model to conditionally generate a stylized image using only a set of examples describing the desired style. The proposed solution produces high-quality images even in the zero-shot setting and allows for greater freedom in changing the content geometry. This is thanks to the introduction of a novel Peer-Regularization Layer that recomposes style in latent space by means of a custom graph convolutional layer aiming at separating style and content. Contrary to the vast majority of existing solutions our model does not require any pre-trained network for computing perceptual losses and can be trained fully end-to-end with a new set of cyclic losses that operate directly in latent space.An extensive ablation study confirms the usefulness of the proposed losses and of the Peer-Regularization Layer, with qualitative results that are competitive with respect to the current state-of-the-art even in the challenging zero-shot setting. This opens the door to more abstract and artistic neural image generation scenarios and easier deployment of the model in. production |
Year | Venue | DocType |
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2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.02913 | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Svoboda, J. | 1 | 6 | 2.10 |
Asha Anoosheh | 2 | 16 | 1.36 |
Christian Osendorfer | 3 | 125 | 13.24 |
Masci, Jonathan | 4 | 1158 | 82.31 |