The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Generative Image Modeling Using Style and Structure Adversarial Networks
Open accessAbstract
Current generative frameworks use end-to-end learning and generate images by sampling from uniform noise distribution. However, these approaches ignore the most basic principle of image formation: images are product of: (a) Structure: the underlying 3D model; (b) Style: the texture mapped onto structure. In this paper, we factorize the image generation process and propose Style and Structure Generative Adversarial Network (\({\text {S}^2}\)-GAN). Our \({\text {S}^2}\)-GAN has two components: the Structure-GAN generates a surface normal map; the Style-GAN takes the surface normal map as input and generates the 2D image. Apart from a real vs. generated loss function, we use an additional loss with computed surface normals from generated images. The two GANs are first trained independently, and then merged together via joint learning. We show our \({\text {S}^2}\)-GAN model is interpretable, generates more realistic images and can be used to learn unsupervised RGBD representations.
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StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.629 · Cited by 3,021
Self-citationPerson Transfer GAN to Bridge Domain Gap for Person Re-identification
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A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,620
Metadata
- Authors
- Wang, X., & Gupta, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 318-335
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2298992465
- Cited by
- 574
- References
- 17
- FWCI
- 34.07
- Type
- conference paper
- Language
- en
- Open access
- green
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