A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from simulated and unsupervised images through adversarial training,” in IEEE Conference on Computer Vision and Pattern Recognition

2017

Cited alongside, same era.

L. Pinto, J. Davidson, and A. Gupta, “Supervision via competition: Robot adversaries for learning tasks,” in International Conference on Robotics and Automation

2017

Cited alongside, same era.

A. Beutel, J. Chen, Z. Zhao, and E. H. Chi, “Data decisions and theoretical implications when adversarially learning fair representations,” arXiv preprint arXiv:1707.00075

Original

2017

Cited alongside, same era.

J. Shen, Y. Qu, W. Zhang, and Y. Yu, “Wasserstein distance guided representation learning for domain adaptation,” arXiv preprint arXiv:1707.01217

Original

2017

Cited alongside, same era.

S. Benaim and L. Wolf, “One-sided unsupervised domain mapping,” in Neural Information Processing Systems

2017

Cited alongside, same era.

T. Bansal, J. Pachocki, S. Sidor, I. Sutskever, and I. Mordatch, “Emergent complexity via multi-agent competition,” arXiv preprint arXiv:1710.03748

Original

2017

Cited alongside, same era.

J. Yoo, H. Ha, J. Yi, J. Ryu, C. Kim, J.-W. Ha, Y.-H. Kim, and S. Yoon, “Energy-based sequence gans for recommendation and their connection to imitation learning,” arXiv preprint arXiv:1706.09200

Original

2017

Cited alongside, same era.

X. Wang, K. Yu, C. Dong, and C. Change Loy, “Recovering realistic texture in image super-resolution by deep spatial feature transform,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

J. Cao, Y. Hu, H. Zhang, R. He, and Z. Sun, “Learning a high fidelity pose invariant model for high-resolution face frontalization,” in Advances in Neural Information Processing Systems

2018

Later among the works it cites.

A. Siarohin, E. Sangineto, S. Lathuilière, and N. Sebe, “Deformable gans for pose-based human image generation,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

H. Chang, J. Lu, F. Yu, and A. Finkelstein, “Pairedcyclegan: Asymmetric style transfer for applying and removing makeup,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

B. Dolhansky and C. Canton Ferrer, “Eye in-painting with exemplar generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

A. Pumarola, A. Agudo, A. M. Martinez, A. Sanfeliu, and F. Moreno-Noguer, “Ganimation: Anatomically-aware facial animation from a single image,” in European Conference on Computer Vision

2018

Later among the works it cites.

C. Donahue, Z. C. Lipton, A. Balsubramani, and J. McAuley, “Semantically decomposing the latent spaces of generative adversarial networks,” in International Conference on Learning Representations

2018

Later among the works it cites.

Z. Shu, M. Sahasrabudhe, R. Alp Guler, D. Samaras, N. Paragios, and I. Kokkinos, “Deforming autoencoders: Unsupervised disentangling of shape and appearance,” in European Conference on Computer Vision

2018

Later among the works it cites.

Y. Lu, Y.-W. Tai, and C.-K. Tang, “Attribute-guided face generation using conditional cyclegan,” in European Conference on Computer Vision

2018

Later among the works it cites.

C. Chan, S. Ginosar, T. Zhou, and A. A. Efros, “Everybody dance now,” arXiv preprint arXiv:1808.07371

Original

2018

Later among the works it cites.

A. Bansal, S. Ma, D. Ramanan, and Y. Sheikh, “Recycle-gan: Unsupervised video retargeting,” in European Conference on Computer Vision

2018

Later among the works it cites.

X. Liang, H. Zhang, L. Lin, and E. Xing, “Generative semantic manipulation with mask-contrasting gan,” in European Conference on Computer Vision

2018

Later among the works it cites.

Y. Chen, Y.-K. Lai, and Y.-J. Liu, “Cartoongan: Generative adversarial networks for photo cartoonization,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

R. Villegas, J. Yang, D. Ceylan, and H. Lee, “Neural kinematic networks for unsupervised motion retargetting,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

Y. Song, C. Ma, X. Wu, L. Gong, L. Bao, W. Zuo, C. Shen, R. W. Lau, and M.-H. Yang, “Vital: Visual tracking via adversarial learning,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

D. Engin, A. Genç, and H. Kemal Ekenel, “Cycle-dehaze: Enhanced cyclegan for single image dehazing,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops

2018

Later among the works it cites.

X. Yang, Z. Xu, and J. Luo, “Towards perceptual image dehazing by physics-based disentanglement and adversarial training,” in AAAI conference on artificial intelligence

2018

Later among the works it cites.

S. Lutz, K. Amplianitis, and A. Smolic, “Alphagan: Generative adversarial networks for natural image matting,” arXiv preprint arXiv:1807.10088

Original

2018

Later among the works it cites.

J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Generative image inpainting with contextual attention,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

X. Liu, Y. Wang, and Q. Liu, “Psgan: a generative adversarial network for remote sensing image pan-sharpening,” in IEEE International Conference on Image Processing

2018

Later among the works it cites.

F. Fang, J. Yamagishi, I. Echizen, and J. Lorenzo-Trueba, “High-quality nonparallel voice conversion based on cycle-consistent adversarial network,” in IEEE International Conference on Acoustics, Speech and Signal Processing

2018

Later among the works it cites.

K. G. Hartmann, R. T. Schirrmeister, and T. Ball, “Eeg-gan: Generative adversarial networks for electroencephalograhic (eeg) brain signals,” arXiv preprint arXiv:1806.01875

Original

2018

Later among the works it cites.

C. Donahue, J. McAuley, and M. Puckette, “Synthesizing audio with generative adversarial networks,” arXiv preprint arXiv:1802.04208

Original

2018

Later among the works it cites.

W. Fedus, I. Goodfellow, and A. M. Dai, “Maskgan: better text generation via filling in the _,” arXiv preprint arXiv:1801.07736

Original

2018

Later among the works it cites.

X. Wang, W. Chen, Y.-F. Wang, and W. Y. Wang, “No metrics are perfect: Adversarial reward learning for visual storytelling,” arXiv preprint arXiv:1804.09160

Original

2018

Later among the works it cites.

P. Qin, W. Xu, and W. Y. Wang, “Dsgan: generative adversarial training for distant supervision relation extraction,” arXiv preprint arXiv:1805.09929

Original

2018

Later among the works it cites.

B. Liu, J. Fu, M. P. Kato, and M. Yoshikawa, “Beyond narrative description: Generating poetry from images by multi-adversarial training,” in ACM Multimedia Conference on Multimedia Conference

2018

Later among the works it cites.

H. Aghakhani, A. Machiry, S. Nilizadeh, C. Kruegel, and G. Vigna, “Detecting deceptive reviews using generative adversarial networks,” in IEEE Security and Privacy Workshops

2018

Later among the works it cites.

Y. Saito, S. Takamichi, and H. Saruwatari, “Statistical parametric speech synthesis incorporating generative adversarial networks,” IEEE/ACM Transactions on Audio, Speech, and Language Processing

2018

Later among the works it cites.

C. Donahue, J. McAuley, and M. Puckette, “Adversarial audio synthesis,” arXiv preprint arXiv:1802.04208

Original

2018

Later among the works it cites.

C. Donahue, B. Li, and R. Prabhavalkar, “Exploring speech enhancement with generative adversarial networks for robust speech recognition,” in IEEE International Conference on Acoustics, Speech and Signal Processing

2018

Later among the works it cites.

A. Gupta and J. Zou, “Feedback gan (fbgan) for dna: A novel feedback-loop architecture for optimizing protein functions,” arXiv preprint arXiv:1804.01694

Original

2018

Later among the works it cites.

T. M. Quan, T. Nguyen-Duc, and W.-K. Jeong, “Compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss,” IEEE Transactions on Medical Imaging

2018

Later among the works it cites.

M. Mardani, E. Gong, J. Y. Cheng, S. S. Vasanawala, G. Zaharchuk, L. Xing, and J. M. Pauly, “Deep generative adversarial neural networks for compressive sensing mri,” IEEE Transactions on Medical Imaging

2018

Later among the works it cites.

Y. Xue, T. Xu, H. Zhang, L. R. Long, and X. Huang, “Segan: Adversarial network with multi-scale l 1 l_{1} loss for medical image segmentation,” Neuroinformatics

2018

Later among the works it cites.

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, “Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Medical Imaging

2018

Later among the works it cites.

G. St-Yves and T. Naselaris, “Generative adversarial networks conditioned on brain activity reconstruct seen images,” in IEEE International Conference on Systems, Man, and Cybernetics

2018

Later among the works it cites.

J.-J. Hwang, S. Azernikov, A. A. Efros, and S. X. Yu, “Learning beyond human expertise with generative models for dental restorations,” arXiv preprint arXiv:1804.00064

Original

2018

Later among the works it cites.

B. Chang, Q. Zhang, S. Pan, and L. Meng, “Generating handwritten chinese characters using cyclegan,” in IEEE Winter Conference on Applications of Computer Vision

2018

Later among the works it cites.

L. Sixt, B. Wild, and T. Landgraf, “Rendergan: Generating realistic labeled data,” Frontiers in Robotics and AI

2018

Later among the works it cites.

D. Xu, S. Yuan, L. Zhang, and X. Wu, “Fairgan: Fairness-aware generative adversarial networks,” in IEEE International Conference on Big Data

2018

Later among the works it cites.

M.-C. Lee, B. Gao, and R. Zhang, “Rare query expansion through generative adversarial networks in search advertising,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2018

Later among the works it cites.

M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification,” Neurocomputing

2018

Later among the works it cites.

A. N. Gomez, S. Huang, I. Zhang, B. M. Li, M. Osama, and L. Kaiser, “Unsupervised cipher cracking using discrete gans,” arXiv preprint arXiv:1801.04883

Original

2018

Later among the works it cites.

B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, R. Lee, S. P. Bhavnani, J. B. Byrd, and C. S. Greene, “Privacy-preserving generative deep neural networks support clinical data sharing,” BioRxiv

2018

Later among the works it cites.

A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

C. Daskalakis, A. Ilyas, V. Syrgkanis, and H. Zeng, “Training gans with optimism,” 2018

2018

Later among the works it cites.

A. Bora, E. Price, and A. G. Dimakis, “Ambientgan: Generative models from lossy measurements,” in International Conference on Learning Representations

2018

Later among the works it cites.

R. D. Hjelm, A. P. Jacob, T. Che, A. Trischler, K. Cho, and Y. Bengio, “Boundary-seeking generative adversarial networks,” in International Conference on Learning Representations

2018

Later among the works it cites.

J. Schmidhuber, “Unsupervised minimax: Adversarial curiosity, generative adversarial networks, and predictability minimization,” arXiv preprint arXiv:1906.04493

Original

2019

Later among the works it cites.

N. Torres-Reyes and S. Latifi, “Audio enhancement and synthesis using generative adversarial networks: A survey,” International Journal of Computer Applications

2019

Later among the works it cites.

Y. Hong, U. Hwang, J. Yoo, and S. Yoon, “How generative adversarial networks and their variants work: An overview,” ACM Computing Surveys

2019

Later among the works it cites.

Z. Wang, Q. She, and T. E. Ward, “Generative adversarial networks: A survey and taxonomy,” arXiv preprint arXiv:1906.01529

Original

2019

Later among the works it cites.

M. Zamorski, A. Zdobylak, M. Zieba, and J. Swiatek, “Generative adversarial networks: recent developments,” in International Conference on Artificial Intelligence and Soft Computing

2019

Later among the works it cites.

Z. Pan, W. Yu, X. Yi, A. Khan, F. Yuan, and Y. Zheng, “Recent progress on generative adversarial networks (gans): A survey,” IEEE Access

2019

Later among the works it cites.

G.-J. Qi, “Loss-sensitive generative adversarial networks on lipschitz densities,” International Journal of Computer Vision

2019

Later among the works it cites.

X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley, “On the effectiveness of least squares generative adversarial networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence

2019

Later among the works it cites.

A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard gan,” in International Conference on Learning Representation

2019

Later among the works it cites.

H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in International Conference on Machine Learning

2019

Later among the works it cites.

A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” in International Conference on Learning representations

2019

Later among the works it cites.

T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

T. Miyato, S.-i. Maeda, S. Ishii, and M. Koyama, “Virtual adversarial training: a regularization method for supervised and semi-supervised learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence

2019

Later among the works it cites.

J. Guan, C. Pan, S. Li, and D. Yu, “Srdgan: learning the noise prior for super resolution with dual generative adversarial networks,” arXiv preprint arXiv:1903.11821

Original

2019

Later among the works it cites.

Z. Ding, X.-Y. Liu, M. Yin, W. Liu, and L. Kong, “Tgan: Deep tensor generative adversarial nets for large image generation,” arXiv preprint arXiv:1901.09953

Original

2019

Later among the works it cites.

L. Q. Tran, X. Yin, and X. Liu, “Representation learning by rotating your faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence

2019

Later among the works it cites.

W. Jiang, S. Liu, C. Gao, J. Cao, R. He, J. Feng, and S. Yan, “Psgan: Pose-robust spatial-aware gan for customizable makeup transfer,” arXiv preprint arXiv:1909.06956

Original

2019

Later among the works it cites.

R. Yi, Y.-J. Liu, Y.-K. Lai, and P. L. Rosin, “Apdrawinggan: Generating artistic portrait drawings from face photos with hierarchical gans,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, “Semantic image synthesis with spatially-adaptive normalization,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

S. Lu, Z. Dou, X. Jun, J.-Y. Nie, and J.-R. Wen, “Psgan: A minimax game for personalized search with limited and noisy click data,” in ACM SIGIR Conference on Research and Development in Information Retrieval

2019

Later among the works it cites.

T. Qiao, J. Zhang, D. Xu, and D. Tao, “Mirrorgan: Learning text-to-image generation by redescription,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

X. Jia, X. Wei, X. Cao, and H. Foroosh, “Comdefend: An efficient image compression model to defend adversarial examples,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

D. Bau, J.-Y. Zhu, H. Strobelt, B. Zhou, J. B. Tenenbaum, W. T. Freeman, and A. Torralba, “Gan dissection: Visualizing and understanding generative adversarial networks,” in International Conference on Learning Representations

2019

Later among the works it cites.

A. Jahanian, L. Chai, and P. Isola, “On the”steerability” of generative adversarial networks,” arXiv preprint arXiv:1907.07171

Original

2019

Later among the works it cites.

B. Zhu, J. Jiao, and D. Tse, “Deconstructing generative adversarial networks,” arXiv preprint arXiv:1901.09465

Original

2019

Later among the works it cites.

K. B. Kancharagunta and S. R. Dubey, “Csgan: cyclic-synthesized generative adversarial networks for image-to-image transformation,” arXiv preprint arXiv:1901.03554

Original

2019

Later among the works it cites.

Y. Wu, J. Donahue, D. Balduzzi, K. Simonyan, and T. Lillicrap, “Logan: Latent optimisation for generative adversarial networks,” arXiv preprint arXiv:1912.00953

Original

2019

Later among the works it cites.

H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. Metaxas, “Stackgan++: Realistic image synthesis with stacked generative adversarial networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence

2019

Later among the works it cites.

H. Tang, D. Xu, N. Sebe, Y. Wang, J. J. Corso, and Y. Yan, “Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

Q. Mao, H.-Y. Lee, H.-Y. Tseng, S. Ma, and M.-H. Yang, “Mode seeking generative adversarial networks for diverse image synthesis,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

A. Uppal, S. Singh, and B. Poczos, “Nonparametric density estimation & convergence of gans under besov ipm losses,” in Neural Information Processing Systems

2019

Later among the works it cites.

S. Athey, G. Imbens, J. Metzger, and E. Munro, “Using wasserstein generative adversial networks for the design of monte carlo simulations,” arXiv preprint arXiv:1909.02210

Original

2019

Later among the works it cites.

T. R. Shaham, T. Dekel, and T. Michaeli, “Singan: Learning a generative model from a single natural image,” in IEEE International Conference on Computer Vision

2019

Later among the works it cites.

A. Shocher, S. Bagon, P. Isola, and M. Irani, “Ingan: Capturing and retargeting the “dna” of a natural image,” in IEEE International Conference on Computer Vision

2019

Later among the works it cites.

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” arXiv preprint arXiv:1912.04958

Original

2019

Later among the works it cites.

M. Lučić, M. Tschannen, M. Ritter, X. Zhai, O. Bachem, and S. Gelly, “High-fidelity image generation with fewer labels,” in International Conference on Machine Learning

2019

Later among the works it cites.

J. Donahue and K. Simonyan, “Large scale adversarial representation learning,” arXiv preprint arXiv:1907.02544

Original

2019

Later among the works it cites.

G. Yildirim, N. Jetchev, R. Vollgraf, and U. Bergmann, “Generating high-resolution fashion model images wearing custom outfits,” arXiv preprint arXiv:1908.08847

Original

2019

Later among the works it cites.

X. Yu, X. Zhang, Y. Cao, and M. Xia, “Vaegan: A collaborative filtering framework based on adversarial variational autoencoders,” in International Joint Conference on Artificial Intelligence

2019

Later among the works it cites.

X. B. Peng, A. Kanazawa, S. Toyer, P. Abbeel, and S. Levine, “Variational discriminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining information flow,” in International Conference on Learning Representations

2019

Later among the works it cites.

I. Albuquerque, J. Monteiro, T. Doan, B. Considine, T. Falk, and I. Mitliagkas, “Multi-objective training of generative adversarial networks with multiple discriminators,” arXiv preprint arXiv:1901.08680

Original

2019

Later among the works it cites.

D. Xu, Y. Wu, S. Yuan, L. Zhang, and X. Wu, “Achieving causal fairness through generative adversarial networks,” in International Joint Conference on Artificial Intelligence

2019

Later among the works it cites.

Z. Wang, Q. She, A. F. Smeaton, T. E. Ward, and G. Healy, “Neuroscore: A brain-inspired evaluation metric for generative adversarial networks,” arXiv preprint arXiv:1905.04243

Original

2019

Later among the works it cites.

K. Kurach, M. Lučić, X. Zhai, M. Michalski, and S. Gelly, “A large-scale study on regularization and normalization in gans,” in International Conference on Machine Learning

2019

Later among the works it cites.

A. Borji, “Pros and cons of gan evaluation measures,” Computer Vision and Image Understanding

2019

Later among the works it cites.

T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby, “Self-supervised generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

S. James, P. Wohlhart, M. Kalakrishnan, D. Kalashnikov, A. Irpan, J. Ibarz, S. Levine, R. Hadsell, and K. Bousmalis, “Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

J. Cao, H. Huang, Y. Li, J. Liu, R. He, and Z. Sun, “Biphasic learning of gans for high-resolution image-to-image translation,” arXiv preprint arXiv:1904.06624

Original

2019

Later among the works it cites.

M. Amodio and S. Krishnaswamy, “Travelgan: Image-to-image translation by transformation vector learning,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

M.-Y. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, and J. Kautz, “Few-shot unsupervised image-to-image translation,” arXiv preprint arXiv:1905.01723

Original

2019

Later among the works it cites.

Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen, “Attgan: Facial attribute editing by only changing what you want,” IEEE Transactions on Image Processing

2019

Later among the works it cites.

M. Liu, Y. Ding, M. Xia, X. Liu, E. Ding, W. Zuo, and S. Wen, “Stgan: A unified selective transfer network for arbitrary image attribute editing,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song, “Generative adversarial user model for reinforcement learning based recommendation system,” in International Conference on Machine Learning

2019

Later among the works it cites.

W. Shang, Y. Yu, Q. Li, Z. Qin, Y. Meng, and J. Ye, “Environment reconstruction with hidden confounders for reinforcement learning based recommendation,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

J. Jordon, J. Yoon, and M. van der Schaar, “Knockoffgan: Generating knockoffs for feature selection using generative adversarial networks,” in International Conference on Learning Representations

2019

Later among the works it cites.

J. Zhang, Z. Wei, I. C. Duta, F. Shen, L. Liu, F. Zhu, X. Xu, L. Shao, and H. T. Shen, “Generative reconstructive hashing for incomplete video analysis,” in ACM International Conference on Multimedia

2019

Later among the works it cites.

Y. Wang, L. Zhang, F. Nie, X. Li, Z. Chen, and F. Wang, “Wegan: Deep image hashing with weighted generative adversarial networks,” IEEE Transactions on Multimedia

2019

Later among the works it cites.

S. C.-X. Li, B. Jiang, and B. Marlin, “Misgan: Learning from incomplete data with generative adversarial networks,” in International Conference on Learning Representations

2019

Later among the works it cites.

C. Wang, C. Xu, X. Yao, and D. Tao, “Evolutionary generative adversarial networks,” IEEE Transactions on Evolutionary Computation

2019

Later among the works it cites.

C. R. Ponce, W. Xiao, P. F. Schade, T. S. Hartmann, G. Kreiman, and M. S. Livingstone, “Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences,” Cell

2019

Later among the works it cites.

Y. Wang, Y. Xia, T. He, F. Tian, T. Qin, C. Zhai, and T.-Y. Liu, “Multi-agent dual learning,” in International Conference on Learning Representations

2019

Later among the works it cites.

M.-K. Xie and S.-J. Huang, “Learning class-conditional gans with active sampling,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

P. Yang, Q. Tan, H. Tong, and J. He, “Task-adversarial co-generative nets,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

D. Bau, J.-Y. Zhu, J. Wulff, W. Peebles, H. Strobelt, B. Zhou, and A. Torralba, “Seeing what a gan cannot generate,” in Proceedings of the IEEE International Conference on Computer Vision

2019

Later among the works it cites.

A. Creswell and A. A. Bharath, “Inverting the generator of a generative adversarial network,” IEEE Transactions on Neural Networks and Learning Systems

2019

Later among the works it cites.

G. Gidel, H. Berard, G. Vignoud, P. Vincent, and S. Lacoste-Julien, “A variational inequality perspective on generative adversarial networks,” in International Conference on Learning Representations

2019

Later among the works it cites.

H. Huang, R. He, Z. Sun, and T. Tan, “Wavelet domain generative adversarial network for multi-scale face hallucination,” International Journal of Computer Vision

2019

Later among the works it cites.

W. Zhang, Y. Liu, C. Dong, and Y. Qiao, “Ranksrgan: Generative adversarial networks with ranker for image super-resolution,” in International Conference on Computer Vision

2019

Later among the works it cites.

A. Duarte, F. Roldan, M. Tubau, J. Escur, S. Pascual, A. Salvador, E. Mohedano, K. McGuinness, J. Torres, and X. Giro-i Nieto, “Wav2pix: speech-conditioned face generation using generative adversarial networks,” in International Conference on Acoustics, Speech and Signal Processing

2019

Later among the works it cites.

B. Gecer, S. Ploumpis, I. Kotsia, and S. Zafeiriou, “Ganfit: Generative adversarial network fitting for high fidelity 3d face reconstruction,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

C. Fu, X. Wu, Y. Hu, H. Huang, and R. He, “Dual variational generation for low-shot heterogeneous face recognition,” in Neural Information Processing Systems

2019

Later among the works it cites.

J. Cao, Y. Hu, B. Yu, R. He, and Z. Sun, “3d aided duet gans for multi-view face image synthesis,” IEEE Transactions on Information Forensics and Security

2019

Later among the works it cites.

Y. Liu, Q. Li, and Z. Sun, “Attribute-aware face aging with wavelet-based generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

A. Clark, J. Donahue, and K. Simonyan, “Efficient video generation on complex datasets,” arXiv preprint arXiv:1907.06571

Original

2019

Later among the works it cites.

H. Wu, S. Zheng, J. Zhang, and K. Huang, “Gp-gan: Towards realistic high-resolution image blending,” 2019

2019

Later among the works it cites.

Y. Han, P. Zhang, W. Huang, Y. Zha, G. D. Cooper, and Y. Zhang, “Robust visual tracking using unlabeled adversarial instance generation and regularized label smoothing,” Pattern Recognition

2019

Later among the works it cites.

W. Liu, X. Hou, J. Duan, and G. Qiu, “End-to-end single image fog removal using enhanced cycle consistent adversarial networks,” arXiv preprint arXiv:1902.01374

Original

2019

Later among the works it cites.

F. Liu, L. Jiao, and X. Tang, “Task-oriented gan for polsar image classification and clustering,” IEEE Transactions on Neural Networks and Learning Systems

2019

Later among the works it cites.

M. Zhang, K. T. Ma, J. Lim, Q. Zhao, and J. Feng, “Anticipating where people will look using adversarial networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence

2019

Later among the works it cites.

D. Li, D. Chen, B. Jin, L. Shi, J. Goh, and S.-K. Ng, “Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks,” in International Conference on Artificial Neural Networks

2019

Later among the works it cites.

S. Yang, J. Liu, W. Wang, and Z. Guo, “Tet-gan: Text effects transfer via stylization and destylization,” in AAAI Conference on Artificial Intelligence

2019

Later among the works it cites.

C. d. M. d’Autume, M. Rosca, J. Rae, and S. Mohamed, “Training language gans from scratch,” arXiv preprint arXiv:1905.09922

Original

2019

Later among the works it cites.

S. Rao and H. Daumé III, “Answer-based adversarial training for generating clarification questions,” arXiv preprint arXiv:1904.02281

Original

2019

Later among the works it cites.

X. Yang, M. Khabsa, M. Wang, W. Wang, A. Awadallah, D. Kifer, and C. L. Giles, “Adversarial training for community question answer selection based on multi-scale matching,” 2019

2019

Later among the works it cites.

Y. Luo, H. Zhang, Y. Wen, and X. Zhang, “Resumegan: An optimized deep representation learning framework for talent-job fit via adversarial learning,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management

2019

Later among the works it cites.

C. Garbacea, S. Carton, S. Yan, and Q. Mei, “Judge the judges: A large-scale evaluation study of neural language models for online review generation,” in Conference on Empirical Methods in Natural Language Processing & International Joint Conference on Natural Language Processing

2019

Later among the works it cites.

B. Tian, Y. Zhang, X. Chen, C. Xing, and C. Li, “Drgan: A gan-based framework for doctor recommendation in chinese on-line qa communities,” in International Conference on Database Systems for Advanced Applications

2019

Later among the works it cites.

M. O. Turkoglu, W. Thong, L. Spreeuwers, and B. Kicanaoglu, “A layer-based sequential framework for scene generation with gans,” arXiv preprint arXiv:1902.00671

Original

2019

Later among the works it cites.

A. El-Nouby, S. Sharma, H. Schulz, D. Hjelm, L. El Asri, S. E. Kahou, Y. Bengio, and G. W. Taylor, “Tell, draw, and repeat: Generating and modifying images based on continual linguistic instruction,” in International Conference on Computer Vision

2019

Later among the works it cites.

N. Ratzlaff and L. Fuxin, “Hypergan: A generative model for diverse, performant neural networks,” in International Conference on Machine Learning

2019

Later among the works it cites.

Q. Wang, H. Yin, H. Wang, Q. V. H. Nguyen, Z. Huang, and L. Cui, “Enhancing collaborative filtering with generative augmentation,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

Y. Zhang, Y. Fu, P. Wang, X. Li, and Y. Zheng, “Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

H. Gao, J. Pei, and H. Huang, “Progan: Network embedding via proximity generative adversarial network,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

B. Hu, Y. Fang, and C. Shi, “Adversarial learning on heterogeneous information networks,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

P. Wang, Y. Fu, H. Xiong, and X. Li, “Adversarial substructured representation learning for mobile user profiling,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Later among the works it cites.

H. Shu, Y. Wang, X. Jia, K. Han, H. Chen, C. Xu, Q. Tian, and C. Xu, “Co-evolutionary compression for unpaired image translation,” in International Conference on Computer Vision

2019

Later among the works it cites.

S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann, “Towards optimal structured cnn pruning via generative adversarial learning,” in IEEE Conference on Computer Vision and Pattern Recognition

2019

Later among the works it cites.

J. Zhang, Y. Peng, and M. Yuan, “Sch-gan: Semi-supervised cross-modal hashing by generative adversarial network,” IEEE Transactions on Cybernetics

2020

Closest in time.

P. Qin, X. Wang, W. Chen, C. Zhang, W. Xu, and W. Y. Wang, “Generative adversarial zero-shot relational learning for knowledge graphs,” in AAAI Conference on Artificial Intelligence

2020

Closest in time.