publication . Article . Other literature type . Preprint . 2017

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Han Zhang; Tao Xu; Hongsheng Li; Shaoting Zhang; Xiaogang Wang; Xiaolei Huang; Dimitris N. Metaxas;
Open Access
  • Published: 19 Oct 2017 Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 41, pages 1,947-1,962 (issn: 0162-8828, eissn: 1939-3539, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-re...
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computational Theory and Mathematics, Software, Applied Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Statistics - Machine Learning, Generative grammar, Image synthesis, Architecture, Image resolution, business.industry, business, Pattern recognition, Still face, Adversarial system, Generative adversarial network, Task analysis, Computer science
52 references, page 1 of 4

[1] M. Arjovsky and L. Bottou. Towards principled methods for training generative adversarial networks. In ICLR, 2017. 1 [OpenAIRE]

[2] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein GAN. arXiv:1701.07875, 2017. 1, 8, 11, 13, 14

[3] A. Brock, T. Lim, J. M. Ritchie, and N. Weston. Neural photo editing with introspective adversarial networks. In ICLR, 2017. 3

[4] T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li. Mode regularized generative adversarial networks. In ICLR, 2017. 1, 3

[5] T. Che, Y. Li, R. Zhang, R. D. Hjelm, W. Li, Y. Song, and Y. Bengio. Maximum-likelihood augmented discrete generative adversarial networks. arXiv:1702.07983, 2017. 1

[6] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS, 2016. 3

[7] E. L. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS, 2015. 3

[8] C. Doersch. Tutorial on variational autoencoders. arXiv:1606.05908, 2016. 4 [OpenAIRE]

[9] I. P. Durugkar, I. Gemp, and S. Mahadevan. Generative multi-adversarial networks. In ICLR, 2017. 3, 11

[10] J. Gauthier. Conditional generative adversarial networks for convolutional face generation. Technical report, 2015. 3

[11] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014. 1, 3, 6

[12] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 5

[13] X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie. Stacked generative adversarial networks. In CVPR, 2017. 3

[14] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015. 5

[15] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In CVPR, 2017. 3

52 references, page 1 of 4
Abstract
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-re...
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computational Theory and Mathematics, Software, Applied Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Statistics - Machine Learning, Generative grammar, Image synthesis, Architecture, Image resolution, business.industry, business, Pattern recognition, Still face, Adversarial system, Generative adversarial network, Task analysis, Computer science
52 references, page 1 of 4

[1] M. Arjovsky and L. Bottou. Towards principled methods for training generative adversarial networks. In ICLR, 2017. 1 [OpenAIRE]

[2] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein GAN. arXiv:1701.07875, 2017. 1, 8, 11, 13, 14

[3] A. Brock, T. Lim, J. M. Ritchie, and N. Weston. Neural photo editing with introspective adversarial networks. In ICLR, 2017. 3

[4] T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li. Mode regularized generative adversarial networks. In ICLR, 2017. 1, 3

[5] T. Che, Y. Li, R. Zhang, R. D. Hjelm, W. Li, Y. Song, and Y. Bengio. Maximum-likelihood augmented discrete generative adversarial networks. arXiv:1702.07983, 2017. 1

[6] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS, 2016. 3

[7] E. L. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS, 2015. 3

[8] C. Doersch. Tutorial on variational autoencoders. arXiv:1606.05908, 2016. 4 [OpenAIRE]

[9] I. P. Durugkar, I. Gemp, and S. Mahadevan. Generative multi-adversarial networks. In ICLR, 2017. 3, 11

[10] J. Gauthier. Conditional generative adversarial networks for convolutional face generation. Technical report, 2015. 3

[11] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014. 1, 3, 6

[12] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 5

[13] X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie. Stacked generative adversarial networks. In CVPR, 2017. 3

[14] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015. 5

[15] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In CVPR, 2017. 3

52 references, page 1 of 4
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