publication . Other literature type . Conference object . Preprint . 2019

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

Mao, Qi; Lee, Hsin-Ying; Tseng, Hung-Yu; Ma, Siwei; Yang, Ming-Hsuan;
  • Published: 01 Jun 2019
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Comment: CVPR 2019. Code: https://github.com/HelenMao/MSGAN
Subjects
free text keywords: Artificial intelligence, business.industry, business, Mode (statistics), Regularization (mathematics), Deep learning, Computer science, Generative grammar, Pattern recognition, Network structure, Categorical variable, Machine learning, computer.software_genre, computer, Image synthesis, Adversarial system, Computer Science - Computer Vision and Pattern Recognition
34 references, page 1 of 3

[1] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In ICML, 2017. 2, 3 [OpenAIRE]

[2] T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li. Mode regularized generative adversarial networks. In ICLR, 2017. 2, 3

[3] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR, 2016. 2, 12

[4] J. Donahue, P. Kra¨henbu¨hl, and T. Darrell. Adversarial feature learning. In ICLR, 2017. 2

[5] V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville. Adversarially learned inference. In ICLR, 2017. 2

[6] I. Durugkar, I. Gemp, and S. Mahadevan. Generative multiadversarial networks. In ICLR, 2017. 2, 3

[7] A. Ghosh, V. Kulharia, V. Namboodiri, P. H. Torr, and P. K. Dokania. Multi-agent diverse generative adversarial networks. In CVPR, 2018. 2, 3

[8] 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. 1, 2

[9] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NIPS, 2017. 2, 4 [OpenAIRE]

[10] X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz. Multimodal unsupervised image-to-image translation. In ECCV, 2018. 1, 2, 3, 4, 5

[11] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In CVPR, 2017. 1, 2, 4, 5, 6, 10, 12

[12] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 10

[13] A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, Citeseer, 2009. 2, 5, 10, 12

[14] C. Ledig, L. Theis, F. Husza´r, 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. 2

[15] H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. K. Singh, and M.-H. Yang. Diverse image-to-image translation via disentangled representations. In ECCV, 2018. 1, 2, 3, 4, 5, 7, 10, 12

34 references, page 1 of 3
Abstract
Comment: CVPR 2019. Code: https://github.com/HelenMao/MSGAN
Subjects
free text keywords: Artificial intelligence, business.industry, business, Mode (statistics), Regularization (mathematics), Deep learning, Computer science, Generative grammar, Pattern recognition, Network structure, Categorical variable, Machine learning, computer.software_genre, computer, Image synthesis, Adversarial system, Computer Science - Computer Vision and Pattern Recognition
34 references, page 1 of 3

[1] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In ICML, 2017. 2, 3 [OpenAIRE]

[2] T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li. Mode regularized generative adversarial networks. In ICLR, 2017. 2, 3

[3] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR, 2016. 2, 12

[4] J. Donahue, P. Kra¨henbu¨hl, and T. Darrell. Adversarial feature learning. In ICLR, 2017. 2

[5] V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville. Adversarially learned inference. In ICLR, 2017. 2

[6] I. Durugkar, I. Gemp, and S. Mahadevan. Generative multiadversarial networks. In ICLR, 2017. 2, 3

[7] A. Ghosh, V. Kulharia, V. Namboodiri, P. H. Torr, and P. K. Dokania. Multi-agent diverse generative adversarial networks. In CVPR, 2018. 2, 3

[8] 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. 1, 2

[9] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NIPS, 2017. 2, 4 [OpenAIRE]

[10] X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz. Multimodal unsupervised image-to-image translation. In ECCV, 2018. 1, 2, 3, 4, 5

[11] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In CVPR, 2017. 1, 2, 4, 5, 6, 10, 12

[12] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 10

[13] A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, Citeseer, 2009. 2, 5, 10, 12

[14] C. Ledig, L. Theis, F. Husza´r, 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. 2

[15] H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. K. Singh, and M.-H. Yang. Diverse image-to-image translation via disentangled representations. In ECCV, 2018. 1, 2, 3, 4, 5, 7, 10, 12

34 references, page 1 of 3
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publication . Other literature type . Conference object . Preprint . 2019

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

Mao, Qi; Lee, Hsin-Ying; Tseng, Hung-Yu; Ma, Siwei; Yang, Ming-Hsuan;