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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.11137 |
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| _version_ | 1866917586448416768 |
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| author | Yin, Liping Chua, Albert |
| author_facet | Yin, Liping Chua, Albert |
| contents | In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not always feasible, and regularization terms can be difficult to tune. Thus, we propose a new set of statistics for texture synthesis based on Sliced Wasserstein Loss, create a multi-scale method to synthesize textures without a user-added spatial tag, study the ability of our proposed method to capture long range constraints, and compare our results to other optimization-based, single texture synthesis algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_11137 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss Yin, Liping Chua, Albert Computer Vision and Pattern Recognition In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not always feasible, and regularization terms can be difficult to tune. Thus, we propose a new set of statistics for texture synthesis based on Sliced Wasserstein Loss, create a multi-scale method to synthesize textures without a user-added spatial tag, study the ability of our proposed method to capture long range constraints, and compare our results to other optimization-based, single texture synthesis algorithms. |
| title | Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2211.11137 |