Abstract
Generative Adversarial Network (GAN) has gained eminence in a very short period as it can learn deep data distributions with the help of a competitive process among two networks. GANs can synthesize images/videos from latent noise with a minimized adversarial cost function. The cost function plays a deciding factor in GAN training and thus, it is often subjected to new modifications to yield better performance. To date, numerous new GAN models have been proposed owing to changes in cost function according to applications. The main objective of this research paper is to present a gist of major GAN publications and developments in image and video field. Several publications were selected after carrying out a thorough literature survey. Beginning from trends in GAN research publications, basics, literature survey, databases for performance evaluation parameters are presented under one umbrella.
Similar content being viewed by others
References
Acharya D (2018) Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs. arXiv,1–22
Agnese J, Herrera J, Tao H, Zhu X (2020) A survey and taxonomy of adversarial neural networks for text-to-image synthesis. Data Min Knowl Disc. https://doi.org/10.1002/widm.1345
Alqahtani H, Kavakli-Thorne M, Kumar G (2019) Applications of generative adversarial networks (GANs): an updated review. Archives Computat Methods Eng. https://doi.org/10.1007/s11831-019-09388-y.1-28
Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. arXiv preprint arXiv:1701.07875
Arora S, Zhang Y (2017) Do GANs actually learn the distribution? An empirical study. arXiv preprint arXiv:1706.08224
Azar MG, Munos R, Kappen HJ (2013) Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. Mach Learn 91(3):325–349
Bansal A, Ma S, Ramanan D, Sheikh Y (2018) Recycle-GAN: Unsupervised video retargeting. In Proceedings of the European Conference on Computer Vision, pp 119–135
BenTaieb A, Hamarneh G (2018) Adversarial stain transfer for histopathology image analysis. IEEE Trans Med Imaging 37(3):792–802
Berg T, Liu J, Woo Lee S, Alexander ML, Jacobs DW, Belhumeur PN (2014) Birdsnap: Large-scale fine-grained visual categorization of birds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2011–2018
Bermudez JD, Happ PN, Feitosa RQ, Oliveira DA (2019) Synthesis of multispectral optical images from SAR/optical multitemporal data using conditional generative adversarial networks. IEEE Geosci Remote Sens Lett 16:1220–1224
Berthelot D, Schumm T, Metz L (2017) BeGAN: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717
Bhattacharjee P, Das S (2017) Temporal coherency based criteria for predicting video frames using deep multi-stage generative adversarial networks. In advances in neural information processing systems, pp 4268-4277
Borji A (2018) Pros and cons of GAN evaluation measures. Comput Vis Image Underst
Brkić K, Hrkać T, Kalafatić Z, Sikirić I (2017) Face hairstyle and clothing colour de-identification in video sequences. IET Signal Process 11(9):1062–1068
Bulat A, Yang J, Tzimiropoulos G (2018) To learn image superresolution, use a GAN to learn how to do image degradation first. ECCV, 185–200
Cao J, Hu Y, Yu B, He R, Sun Z (2019) 3D aided duet GANs for multi-view face image synthesis. IEEE Trans Inform Forensics Secur 14:2028–2042
Cao Y, Jia LL, Chen YX, Lin N, Yang C, Zhang B, Liu Z, Li X, Dai H (2019) Recent advances of generative adversarial networks in computer vision. IEEE Access, 14985–15006, 7
Chang W, Yang G, Yu J, Liang Z (2018) Real-time segmentation of various insulators using generative adversarial networks. IET Comput Vis 12(5):596–602
Che T, Li Y, Jacob AP, Bengio Y, Li W (2016) Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136
Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016) InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems, pp 2172–2180
Chen K Y, Lu C Y, Xing Y (2017) Video super-resolution using temporal fusion generative adversarial network
Chen X, Yu J, Kong S, Wu Z, Fang X, Wen L (2019) Towards real-time advancement of underwater visual quality with GAN. IEEE Trans Ind Electron
Choi Y, Choi M, Kim M, Ha JW, 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
Chu M, Xie Y, Mayer J, Leal-Taixe L, Thuerey N (2018)Learning Temporal Coherence via Selfsupervision for GAN-based Video generation. arXiv,1–22
Coates A, Ng A, Lee H (2011) An analysis of single-layer Networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 215–223
Costa P, Galdran A, Meyer MI, Niemeijer M, Abràmoff M, Mendonça AM, Campilho A (2018) End-to-end Adversarial retinal image synthesisIEEE transactions on medical imaging 37(3):781–791
Cousins S, Shawe-Taylor J (2017) High-probability minimax probability machines. Mach Learn 106(6):863–886
Demir U, Unal G (2018) Patch-based image inpainting with generative adversarial networks. arXiv preprint arXiv:1803.07422
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database
Denton EL, Chintala, Fergus R (2015) Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems, pp 1486–1494
Dinh L, Sohl-Dickstein J, Bengio S (2016) Density estimation using real nvp. arXiv preprint arXiv:1605.08803
Donahue J, Krähenbühl P, Darrell T (2016) Adversarial feature learning. arXiv preprint arXiv:1605.09782
Dong J, Yin R, Sun X, Li Q, Yang Y, Qin X (2019) Inpainting of remote sensing SST images with deep convolutional generative adversarial network. IEEE Geosci Remote Sens Lett 16(2):173–177
Fahlman SE, Hinton GE, Sejnowski TJ (1983) Massively parallel architectures for Al: NETL thistle and Boltzmann machines. In National Conference on Artificial Intelligence
Frey BJ, Hinton GE, Dayan P (1996) Does the wake-sleep algorithm produce good density estimators? In Advances in neural information processing systems, pp 661–667
Frey BJ, Brendan JF, Frey BJ (1998) Graphical models for machine learning and digital communication. MIT press
Gao Y, Liu Y, Wang Y, Shi Z, Yu J (2019) A universal intensity standardization method based on a many-to-one weak-paired cycle generative adversarial network for magnetic resonance images. IEEE Trans Med Imaging 38:2059–2069
Ge H, Yao Y, Chen Z, Sun L (2018) Unsupervised transformation network based on GANs for target-domain oriented image translation. IEEE Access 6:61342–61350
Ghamisi P, Yokoya N (2018) Img2dsm: height simulation from single imagery using conditional generative adversarial net. IEEE Geosci Remote Sens Lett 15(5):794–798
Gong M, Niu X, Zhang P, Li Z (2017) Generative Adversarial Networks for change detection in multispectral imagery. IEEE Geosci Remote Sens Lett 14(12):2310–2314
Goodfellow IJ, Bulatov Y, Ibarz J, Arnoud S, Shet V (2013) Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S and Bengio Y (2014) Generative Adversarial nets. In Advances in neural information processing systems, pp 2672–2680
Gretton A, Borgwardt KM, Rasch MJ, Schölkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13:723–773
Griffin G, Holub A, Perona P (2007) Caltech-101 object category dataset
Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset
Grzegorczyk M (2016) A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points. Mach Learn 102(2):155–207
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein GANs. In Advances in Neural Information Processing Systems, pp 5767–5777
Gurumurthy S, Kiran Sarvadevabhatla R, Venkatesh Babu R (2017) DeliGAN: Generative Adversarial Networks for diverse and limited data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 166–174
He Y, Zhang J, Shan H, Wang L (2019) Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans Inform Forensics Secur 14(1):102–113
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 equilibriumIn advances in neural information processing systems, pp 6626-6637
Hinton GE, Sejnowski TJ (1986) Learning and relearning in Boltzmann machines parallel distributed processing: explorations in the microstructure of cognition 1(282-317) 2
Hu B, Tang Y, Chang EI, Fan Y, Lai M, Xu Y (2017) Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks. arXiv preprint arXiv:1711.11317
Huang Z, Paudel DP, Li G, Wu J, Timofte R, Gool LV (2019) Divide-and-Conquer Adversarial Learning for High-resolution Image and Video enhancement, arXiv,1–17
Huo Y, Xu Z, Bao S, Bermudez C, Moon H, Parvathaneni P, Landman BA (2018) Splenomegaly segmentation on multi-modal MRI using deep convolutional networks. IEEE Trans Med Imaging
Im DJ, Kim CD, Jiang H, Memisevic R (2016) Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110
Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional Adversarial Networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125–1134
Jolicoeur-Martineau A (2018) The relativistic discriminator: a key element missing from standard GAN. arXiv preprint arXiv:1807.00734
Juefei-Xu F, Boddeti VN, Savvides M (2017) GANg of GANs: generative adversarial networks with maximum margin ranking. arXiv preprint arXiv:1704.04865
Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of GANs for improved quality and stability and variation. arXiv preprint arXiv:1710.10196
Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp 1857–1865. JMLRorg
Kim D, Jang HU, Mun SM, Choi S, Lee HK (2018) Median filtered image restoration and anti-forensics using adversarial networks. IEEE Signal Process Lett 25(2):278–282
Kingma DP, Salimans T, Jozefowicz R, Chen X, Sutskever I, Welling M (2016) Improving variational inference with inverse autoregressive flow. In Advances in Neural Information Processing Systems. arXiv:1606.04934
Kodali N, Abernethy J, Hays J, Kira Z (2017) On convergence and stability of GANs. arXiv preprint arXiv:1705.07215
Krizhevsky A, Hinton G (2010) Convolutional deep belief networks on cifar-10. Unpublished manuscript 40(7)
LeCun Y (1998) The MNIST database of handwritten digits. https://yann.lecun.com/exdb/mnist/
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
Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, (2017) Photo-realistic single image superresolution using a generative adversarial network. CVPR
Lee HJ, Kim ST, Lee H, Ro YM (2019) Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network. IEEE Trans Circuits Syst Video Technol
Lehmann EL, Romano JP (2006) Testing statistical hypotheses. Springer, Science and Business Media
Li Y, Shen L (2018) cC-GAN: a robust transfer-learning framework for HEp-2 specimen image segmentation. IEEE Access 6:14048–14058
Li J, Liu S, He H, Li L (2018) A novel framework for gear safety factor prediction. IEEE Trans Industrial Informatics
Li H, Li G, Lin L, Yu H, Yu Y (2018) Context-aware semantic inpainting. IEEE transactions on cybernetics
Li J, Skinner KA, Eustice RM, Matthew JR (2018) WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robotics and Automation Letters 3(1):387–394
Li J, He H, Li L, Chen G (2019) A novel generative model with bounded-GAN for reliability classification of gear safety. IEEE Trans Ind Electron 66(11):8772–8781
Li P, Prieto L, Mery D, Flynn PJ (2019) On low-resolution face recognition in the wild: comparisons and new techniques. IEEE Trans Inform Forensics Secur 14:2000–2012
Liang X, Lee L, Dai W, Xing EP (2017) Dual motion GAN for future-flow embedded video prediction. In proceedings of the IEEE international conference on computer vision, pp 1744–1752
Liao K, Lin C, Zhao Y, Gabbouj M (2020) DR-GAN: automatic radial distortion rectification using conditional GAN in real-time. IEEE Trans Circuits Syst Video Technol 30(3):725–733
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, Cham, pp 740–755
Lin D, Fu K, Wang Y, Xu G, Sun X (2017) MARTA GANs: unsupervised representation learning for remote sensing image classification. IEEE Geosci Remote Sens Lett 14(11):2092–2096
Liu Z, Luo P, Wang X, Tang X (2018) Large-scale celebfaces attributes (celeba) dataset
Lopez-Tapia S, Lucas A, Molina R, Katsaggelos A K (2018) A single video super-resolution GAN for multiple Downsampling operators based on pseudo-inverse image formation models
Lopez-Tapia S, Lucas A, Molina R, Katsaggelos A K (2019) GAN-based video super-resolution with direct regularized inversion of the low-resolution formation model, ICIP conference
Lucas A, Lopez-Tapiad S, Molinae R, Katsaggelos AK (2019) Generative adversarial networks and perceptual losses for video super-resolution. IEEE Trans Image Process 28:3312–3327
Lucas A, Lopez-Tapia S, Molina R, Katsaggelos AK (2019) Generative adversarial networks and perceptual losses for video super-resolution. IEEE Trans Image Process 28(7):3312–3327
Lucic M, Kurach K, Michalski M, Gelly S, Bousquet O (2018) Are GANs created equal? A large-scale study. In Advances in neural information processing systems, pp 698–707
Lucic M, Kurach K, Michalski M, Bousquet O, Gelly S (2018) Are GANs created equal? A large-scale study. NeurIPS 1–10
Ma S, Fu J, Wen Chen C, Mei T (2018) DA-GAN: Instance-level image translation by deep attention Generative Adversarial Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5657–5666
Ma D, Tang P, Zhao L (2019) SiftingGAN: generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro. IEEE Geosci Remote Sens Lett 16:1046–1050
Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S (2017) Least squares generative adversarial networks. In proceedings of the IEEE international conference on computer vision, pp 2794–2802
Mao X, Li Q, Xie H, KLau RY, Wang Z, Smolley SP (2018) On the effectiveness of least squares generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41:2947–2960. https://doi.org/10.1109/TPAMI.2018.2872043
Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM (2019) Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans Med Imaging 38(1):167–179
Mathieu M, Couprie C, Le Y (2016) Deep multi-scale video prediction beyond mean square error. ICLR:1–14
Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784
Miyato T, Kataoka T, Koyama M, Yoshida Y (2018) Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957
Nie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, Shen D (2018) Medical image synthesis with deep convolutional adversarial networks. IEEE Trans Biomed Eng 65(12):2720–2730
Nilsback ME and Zisserman A (2008). Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision Graphics and Image Processing, pp 722–729, IEEE
Niu X, Gong M, Zhan T, Yang Y (2019) A conditional adversarial network for change detection in heterogeneous images. IEEE Geosci Remote Sens Lett 16(1):45–49
Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier GANs. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp 2642–2651, JMLRorg
Ohnishi K, Yamamoto S, Ushiku Y, Harada T (2018) Hierarchical video generation from orthogonal information: optical flow and texture. In Thirty-Second AAAI Conference on Artificial Intelligence
Oliveira DA, Ferreira RS, Silva R, Brazil EV (2018) Interpolating seismic data with conditional generative adversarial networks. IEEE Geosci Remote Sens Lett 99:1–5
Oord AVD, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. arXiv preprint arXiv:1609.03499
Ouyang X, Cheng Y, Jiang Y, Li C L, Zhou P (2018) Pedestrian-synthesis-GAN: generating pedestrian data in real scene and beyond.arXiv 1–22
Pan Y, Qiu Z, Yao T, Li H, Mei T (2017) To create what you tell: generating videos from captions. In proceedings of the 25th ACM international conference on multimedia, pp 1789–1798. ACM
Pang Y, Xie J, Li X (2018) Visual haze removal by a unified generative adversarial network. IEEE Trans Circuits Syst Video Technol
Pascual S, Bonafonte A, Serrà J (2017) SEGAN: speech enhancement generative adversarial network. arXiv preprint arXiv:1703.09452
Quan TM, Nguyen-Duc T, Jeong WK (2018) Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging 37(6):1488–1497
Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434
Rezende DJ, Mohamed S (2015) Variational inference with normalizing flows. arXiv preprint arXiv:1505.05770
Richardson E, Weiss Y (2018) On GANs and gmms. In Advances in Neural Information Processing Systems, pp 5852–5863
Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K (2017) XGAN: unsupervised image-to-image translation for many-to-many mappings. arXiv preprint arXiv:1711.05139
Saito Y, Takamichi S, Saruwatari H (2018) Statistical parametric speech synthesis incorporating generative adversarial networks. IEEE/ACM Trans Audio Speech Language Process 26(1):84–96
Sakkos D, Ho ES, Shum HP (2019) Illumination-aware multi-task GANs for foreground segmentation. IEEE Access, Illumination-Aware Multi-Task GANs for Foreground Segmentation
Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training GANs. In Advances in neural information processing systems, pp 2234–2242
Shan H, Zhang Y, Yang Q, Kruger U, Kalra MK, Sun L, Wang G (2018) 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Trans Med Imaging 37(6):1522–1534
Shen Z, Sheng W, Xu L T, Kautz J, Yang M H (2018) Deep Semantic Face Deblurring, CVPR
Shen Z, Wang W, Lu X, Shen J, Ling H, Xu T, Shao L (2019) Human-Aware Motion Deblurring, ICCV
Shi Y, Li Q, Zhu XX (2018) Building footprint generation using improved generative adversarial networks. IEEE Geosci Remote Sens Lett
Snell J, Ridgeway K, Liao R, Roads BD, Mozer MC, Zemel RS (2017) Learning to generate images with perceptual similarity metrics. In 2017 IEEE International Conference on Image Processing, pp 4277–4281. IEEE
Theis L, Oord AVD, Bethge M (2015) A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844
Tuan YL, Lee HY (2019) Improving conditional sequence generative adversarial networks by stepwise evaluation. IEEE/ACM Trans Audio Speech Language Process 27(4):788–798
Tulyakov S, Liu MY, Yang X, Kautz J (2018) MocoGAN: Decomposing motion and content for video generation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1526–1535
Van Horn G, Branson S, Farrell R, Haber S, Barry J, Ipeirotis P, Belongie S (2015) Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 595–604
Van Horn G, Mac Aodha O, Song Y, Shepard A, Adam H, Perona P, Belongie S (2017) The inaturalist challenge 2017 dataset. arXiv preprint arXiv:1707.06642 1(2)
Vondrick C, Pirsiavash H, Torralba A (2016) Generating videos with scene dynamics. In Advances In Neural Information Processing Systems, pp 613–621
Walker J, Marino K, Gupta A, Hebert M (2017) The pose knows: video forecasting by generating pose futures. In proceedings of the IEEE international conference on computer vision, pp 3332–3341
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang Y, Zhang L, van de Weijer J (2016) Ensembles of generative adversarial networks. arXiv preprint arXiv:1612.00991
Wang W, Huang Q, You S, Yang C, Neumann U (2017) Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. In Proceedings of the IEEE international conference on computer vision, pp 2298–2306
Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Loy CC (2018) EsrGAN: enhanced super-resolution generative adversarial networks. In: European Conference on Computer Vision. Springer, Cham, pp 63–79
Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush DS, Shen D (2018) 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans Med Imaging
Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush DS, Shen D (2018) 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans Med Imaging
Wang Z, Chen Z, Wu F (2018) Thermal to visible facial image translation using generative adversarial networks. IEEE Signal Process Lett 25(8):1161–1165
Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P (2010) Caltech-UCSD birds 200
Wen S, Liu W, Yang Y, Huang T, Zeng Z (2018) Generating realistic videos from keyframes with concatenated GANs. IEEE Trans Circuits Syst Video Technol
Wolterink JM, Leiner T, Viergever MA, Išgum I (2017) Generative Adversarial Networks for noise reduction in low-dose CT. IEEE Transactions on Medical Imaging 36(12):2536–2545
Wu W, Qi H, Rong Z, Liu L, Su H (2018) Scribble-supervised segmentation of aerial building footprints using adversarial learning. IEEE Access 6:58898–58911
Xiang S, Li H (2017) On the effects of batch and weight normalization in generative adversarial networks. arXiv preprint arXiv:1704.03971
Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747
Xiong W, Luo W, Ma L, Liu W, Luo J (2018) Learning to generate time-lapse videos using multi-stage dynamic Generative Adversarial Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2364–2373
Xu C, Ren J, Zhang D, Zhang Y, Qin Z, Ren K (2019) GANobfuscator: mitigating information leakage under GAN via differential privacy. IEEE Trans Inform Forensics Secur 14:2358–2371
Xuan Q, Chen Z, Liu Y, Huang H, Bao G, Zhang D (2018) Multi-view generative adversarial network and its application in pearl classification. IEEE Trans Ind Electron 66(10):8244–8252
Yang J, Kannan A, Batra D, Parikh D (2017) Lr-GAN: layered recursive generative adversarial networks for image generation. arXiv preprint arXiv:1703.01560
Yang G, Yu S, Dong H, Slabaugh G, Dragotti PL, Ye X, Firmin D (2018) DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans Med Imaging 37(6):1310–1321
Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Wang G (2018) Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 37(6):1348–1357
Yang Y, Zhou J, Ai J, Bin Y, Hanjalic A, Shen HT, Ji Y (2018) Video captioning by adversarial lstm. IEEE Trans Image Process 27(11):5600–5611
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
Yu F, Seff A, Zhang Y, Song S, Funkhouser T, Xiao J (2015) LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365
Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2018) Free-form image inpainting with gated convolution. arXiv preprint arXiv:1806.03589
Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2018) Generative image inpainting with contextual attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5505–5514
Yu B, Zhou L, Wang L, Shi Y, Fripp J, Bourgeat P (2019) Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Transactions on Medical Imaging
Yuan Y, Tian C, Lu X (2018) Auxiliary Loss Multimodal GRU Model in Audio-Visual Speech Recognition. IEEE Access 6:5573–5583
Zeng Y, Lu H, Borji A (2017) Statistics of deep generated images. arXiv preprint arXiv:1708.02688
Zhan Y, Hu D, Wang Y, Yu X (2018) Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geosci Remote Sens Lett 15(2):212–216
Zhang L, Liu P, Gulla (2019) J Artificial Intell Mach Learning https://doi.org/10.1007/s10994-018-05777-9
Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, Metaxas DN (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
Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, Metaxas D (2017) StackGAN++: realistic image synthesis with stacked generative adversarial networks. arXiv preprint arXiv:1710.10916
Zhang M, Gong M, Mao Y, Li J, Wu Y (2018) Unsupervised feature extraction in Hyperspectral images based on Wasserstein generative adversarial network. IEEE Trans Geosci Remote Sens
Zhang H, Goodfellow I, Metaxas D, Odena A (2018) Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318
Zhang Z, Song Y, Qi H (2018) Decoupled learning for conditional Adversarial NetworksIn 2018 IEEE Winter Conference on Applications of Computer Vision, pp 700–708. IEEE
Zhang K, Luo W, Zhong Y, Ma L, Liu W, Li H (2019) Adversarial spatio-temporal learning for video deblurring. IEEE Trans Image Process 28(1):291–301
Zhao J, Mathieu M, LeCun Y (2016) Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126
Zhou Z, Cai H, Rong S, Song Y, Ren K, Zhang W, Wang J (2017) Activation maximization generative adversarial nets. arXiv preprint arXiv:1703.02000
Zhu JY, Zhang R, Pathak D, Darrell T, Efros AA, Wang O, Shechtman E (2017) Toward multimodal image-to-image translation. In Advances in Neural Information Processing Systems, pp 465–476
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision, pp 2223–2232
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. ICCV
Zhu L, Chen Y, Ghamisi P, Benediktsson JA (2018) Generative adversarial networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(9):5046–5063
Zhu X, Zhang L, Zhang L, Liu X, Shen Y, Zhao S (2020) GAN-based image super-resolution with a novel quality loss. Math Probl Eng 2020:1–12. https://doi.org/10.1155/2020/5217429
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sharma, A., Jindal, N. & Rana, P.S. Potential of generative adversarial net algorithms in image and video processing applications– a survey. Multimed Tools Appl 79, 27407–27437 (2020). https://doi.org/10.1007/s11042-020-09308-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09308-4