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GAN Papers with 1k+ citations

tags: GAN

Updated on Feb. 2021

  1. (Vanilla GAN)Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial networks. arXiv preprint arXiv:1406.2661. Cited by 27497, 1st GAN paper
  2. (DCGAN)Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. Cited by 8447, improve CNN in GAN using striding
  3. (Pix2Pix)Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134). Cited by 7899, Nvidia change noise-to-picture to picture-to-picture
  4. (cycleGAN)Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2223-2232). Cited by 7325, most well-known cycle loss with pix2pix discrimination
  5. (WGAN)Arjovsky, M., Chintala, S., & Bottou, L. (2017, July). Wasserstein generative adversarial networks. In International conference on machine learning (pp. 214-223). PMLR. Cited by 6202, replace JS divergence with Wasserstein distance
  6. (SRGAN)Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., … & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690). Cited by 5026, the most well-known super-resolution paper
  7. (conditional GAN)Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. Cited by 4729, 1st conditional GAN
  8. (WGAN-GP)Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028.Cited by 4187, accelerated WGAN
  9. (DANN)Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., … & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The journal of machine learning research, 17(1), 2096-2030., Cited by 2737 Domain Adaptation
  10. (ProGAN)Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196., Cited by 2597, layer by layer learning improve quality
  11. (InfoGAN)Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657. Cited by 2522, adding conditional variables into input noise for conditional GAN
  12. (2time scale update)Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. arXiv preprint arXiv:1706.08500. Cited by 2235, run discriminator more times to improve training
  13. (LSGAN)Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., & Paul Smolley, S. (2017). Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2794-2802). Cited by 1991, replace Wasserstein distance with JS divergence and L2 error of a linear output
  14. (ADDA)Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7167-7176). Cited by 1924, apply GAN on classifier domain transfer
  15. (GAN-CLS)Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016, June). Generative adversarial text to image synthesis. In International Conference on Machine Learning (pp. 1060-1069). PMLR.Cited by 1860, GAN text to image synthesis
  16. (LAPGAN)Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015). Deep generative image models using a laplacian pyramid of adversarial networks. arXiv preprint arXiv:1506.05751. Cited by 1791, learning self upsample
  17. (Spectral Normalization)Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957. Cited by 1750, real 1-Lipschitz GAN loss
  18. (styleGAN)Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401-4410). Cited by 1634, AdaIN style transfer with high quality
  19. (stackGAN)Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. N. (2017). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 5907-5915).Cited by 1475, text to image 2 stage image improvement
  20. (BigGAN)Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096. Cited by 1455, stabilize training with large batch + truncated sampling input noise
  21. (UNIT)Liu, M. Y., Breuel, T., & Kautz, J. (2017). Unsupervised image-to-image translation networks. arXiv preprint arXiv:1703.00848.Cited by 1441, common latent space for domain A and B
  22. (StarGAN)Choi, Y., Choi, M., Kim, M., Ha, J. W., Kim, S., & Choo, J. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8789-8797).Cited by 1398, multiple cross-domain style transfer
  23. (MIXUP)Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412. Cited by 1389, change Empirical Risk Minimization to Vincinal Risk Minimization for data augmentation
  24. (SeqGAN)Yu, L., Zhang, W., Wang, J., & Yu, Y. (2017, February). Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).Cited by 1330, RL sequence to sequence GAN
  25. (SAGANs)Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2019, May). Self-attention generative adversarial networks. In International conference on machine learning (pp. 7354-7363). PMLR.Cited by 1327, intra-image self-attention GAN
  26. (DiscoGAN)Kim, T., Cha, M., Kim, H., Lee, J. K., & Kim, J. (2017, July). Learning to discover cross-domain relations with generative adversarial networks. In International Conference on Machine Learning (pp. 1857-1865). PMLR.Cited by 1180, cycle GAN with single discrimination
  27. (Problem of JS divergence)Arjovsky, M., & Bottou, L. (2017). Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862.Cited by 1140, JS divergence cannot distinguish between those untouched conditions
  28. (GAIL)Ho, J., & Ermon, S. (2016). Generative adversarial imitation learning. arXiv preprint arXiv:1606.03476.Cited by 1083, GAN in imitation learning in RL
  29. (3D-GAN)Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016). Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. arXiv preprint arXiv:1610.07584.Cited by 1063, GAN 3d object
  30. (CyCADA)Hoffman, J., Tzeng, E., Park, T., Zhu, J. Y., Isola, P., Saenko, K., … & Darrell, T. (2018, July). Cycada: Cycle-consistent adversarial domain adaptation. In International conference on machine learning (pp. 1989-1998). PMLR. Cited by 1066, cycle GAN in domain adaptation
  31. (DualGAN)Yi, Z., Zhang, H., Tan, P., & Gong, M. (2017). Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE international conference on computer vision (pp. 2849-2857). Cited by 1014, cycle GAN with U-Net

Type of Papers (order by publish time)

Origian GAN type

2014 Vanilla GAN, 27497 cites
2014 conditional GAN, 4729 cites
2015 DCGAN, 8447 cites

Unpaired GAN

2017 cycleGAN, 7325 cites
2017 UNIT, 1441 cites
2017 DualGAN, 1014 cites
2017 DiscoGAN, 1180 cites
2018 StarGAN, 1398 cites

Higher resolution

2015 LAPGAN (generated resolution), 1791 cites
2016 SRGAN (upsamled resolution), 5026 cites
2017 ProGAN (generated resolution), 2597 cites
2018 SAGANs (generated resolution), 1327 cites
2019 styleGAN (generated resolution), 1634 cites

Better Embedding Condition

2016 InfoGAN, 2522 cites
2016 Pix2Pix, 7899 cites
2017 MIXUP, 1389 cites
2018 BigGAN, 1455 cites

Handeling optimization issues

2016 LSGAN, 1991 cites
2017 Problem of JS divergence, 1140 cites
2017 WGAN, 6202 cites
2017 WGAN-GP, 4187 cites
2017 2time scale update, 2235 cits
2018 Spectral Normalization, 1750 cites

Domain Adaptation

2016 DANN, 2737 cites
2017 ADDA, 1924 cites
2017 CyCADA, 1066 cites

Text to image

2016 GAN-CLS, 1860 cites
2017 stackGAN, 1475 cites

Text to Text

2017 SeqGAN, 1330 cites

RL

2016 GAIL, 1083 cites

3D object synthesis

2016 3D-GAN, 1063 cites

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