Semantic Image Synthesis via Adversarial Learning

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 5707-5715

  Dong, Hao ^a   Yu, Simiao ^a   Wu, Chao ^a   Guo, Yike ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; SEMANTICS;

ADVERSARIAL LEARNING; IMAGE MANIPULATION; NATURAL LANGUAGES; NEURAL ARCHITECTURES; ORIGINAL IMAGES; REALISTIC IMAGES; SEMANTIC IMAGES; SEMANTIC INFORMATION;

IMAGE PROCESSING;

EID: 85041901593     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.608     Document Type: Conference Paper

References (42)

1. A. Brock, T. Lim, J. M. Ritchie, and N. Weston. Neural Photo Editing with Introspective Adversarial Networks. In ICLR, 2017.
2. 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. E. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. In NIPS, 2015.
4. J. Donahue, P. Krähenbühl, and T. Darrell. Adversarial Feature Learning. In ICLR, 2017.
5. A. Dosovitskiy, J. Tobias Springenberg, and T. Brox. Learning to Generate Chairs with Convolutional Neural Networks. In CVPR, 2015.
6. V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville. Adversarially Learned Inference. In ICLR, 2017.
7. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative Adversarial Nets. In NIPS, 2014.
8. K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In ICML, 2015.
9. K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016.
10. S. Ioffe and C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML, 2015.
11. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-Image Translation with Conditional Adversarial Networks. In CVPR, 2017.
12. J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. In ECCV, 2016.
13. D. Kingma and J. Ba. Adam: A Method for Stochastic Optimization. In ICLR, 2014.
14. D. P. Kingma and M. Welling. Auto-Encoding Variational Bayes. In ICLR, 2014.
15. R. Kiros, R. Salakhutdinov, and R. Zemel. Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. In TACL, 2015.
16. A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther. Autoencoding Beyond Pixels Using a Learned Similarity Metric. In ICML, 2016.
17. C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. In CVPR, 2017.
18. C. Li and M. Wand. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. In ECCV, 2016.
19. M.-Y. Liu, T. Breuel, and J. Kautz. Unsupervised Image-to-Image Translation Networks. ArXiv preprint arXiv: 1703. 00848, 2017.
20. A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey. Adversarial Autoencoders. In ICLR, 2016.
21. M. Mirza and S. Osindero. Conditional Generative Adversarial Nets. ArXiv preprint arXiv: 1411. 1784, 2014.
22. M.-E. Nilsback and A. Zisserman. Automated Flower Classification over a Large Number of Classes. In ICCVGIP, 2008.
23. A. V. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu. Pixel Recurrent Neural Networks. In ICML, 2016.
24. A. V. d. Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu. Conditional Image Generation with PixelCNN Decoders. In NIPS, 2016.
25. G. Perarnau, J. V. D. Weijer, B. Raducanu, J. M. Álvarez, and D. Csiro. Invertible Conditional GANs for Image Editing. In NIPS Workshop, 2016.
26. A. Radford, L. Metz, and S. Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In ICLR, 2016.
27. S. Reed, Z. Akata, H. Lee, and B. Schiele. Learning Deep Representations of Fine-Grained Visual Descriptions. In CVPR, 2016.
28. S. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee. Learning What and Where to Draw. In NIPS, 2016.
29. S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee. Generative Adversarial Text to Image Synthesis. In ICML, 2016.
30. S. Reed, Y. Zhang, Y. Zhang, and H. Lee. Deep Visual Analogy-Making. In NIPS, 2015.
31. D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In ICML, 2014.
32. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein. Imagenet Large Scale Visual Recognition Challenge. IJCV, 115 (3): 211-252, 2015.
33. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved Techniques for Training GANs. In NIPS, 2016.
34. K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR, 2015.
35. Y. Taigman, A. Polyak, and L. Wolf. Unsupervised Cross-Domain Image Generation. In ICLR, 2017.
36. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011.
37. X. Wang and A. Gupta. Generative Image Modeling Using Style and Structure Adversarial Networks. In ECCV, 2016.
38. X. Yan, J. Yang, K. Sohn, and H. Lee. Attribute2Image: Conditional Image Generation from Visual Attributes. In ECCV, 2016.
39. J. Yang, S. Reed, M.-H. Yang, and H. Lee. Weaklysupervised Disentangling with Recurrent Transformations for 3D View Synthesis. In NIPS, 2015.
40. D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon. Pixel-Level Domain Transfer. In ECCV, 2016.
41. H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. ArXiv preprint arXiv: 1612. 03242, 2016.
42. J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros. Generative Visual Manipulation on the Natural Image Manifold. In ECCV, 2016.