Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 18, 08 December 2023
Open
Access | Article
Unpaired image neural style transfer based on Non-Local-Attention-Cycle-Consistent adversarial network
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Abstract
Existing image translation methods already enable style transfer on unpaired data. Although these methods have yielded satisfactory results, they still result in changing the background while changing the object. One reason is that when using convolutional neural networks, global information is lost as the number of network layers increases, and the absence of an effective sensory field leads to the failure to generate high-quality results. This paper proposed a Non-Local-Attention-Cycle-Consistent Adversarial Networks for unpaired images style transfer. The no-local-attention can quickly capture long-range dependencies, better extracts global information, ensures effective focus on the foreground while preserving the background, and can be easily embedded into the current network architecture. Experiments are conducted on neural style transfer task with public dataset, this model can obtain the better result than CycleGAN. It allows better attention to structural features rather than just textural features. It can reconstruct some of the content lost by CycleGAN. Recent research has also demonstrated that the optimizer has an impact on the performance of the network. This paper applies the Nadam optimizer and find that this improves training process.
Keywords
No-local Attention, CycleGAN, Style Transfer
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-201-5
- ISBN (Online)
- 978-1-83558-202-2
- Published Date
- 08 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/18/20230364
- Copyright
- 08 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited