Skip to main content
SpringerLink
Log in

Applications of Generative Adversarial Networks (GANs): An Updated Review

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Generative adversarial networks (GANs) present a way to learn deep representations without extensively annotated training data. These networks achieve learning through deriving back propagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in several applications. GANs have made significant advancements and tremendous performance in numerous applications. The essential applications include semantic image editing, style transfer, image synthesis, image super-resolution and classification. This paper aims to present an overview of GANs, its different variants, and potential application in various domains. The paper attempts to identify GANs’ advantages, disadvantages and significant challenges to the successful implementation of GAN in different application areas. The main intention of this paper is to explore and present a comprehensive review of the crucial applications of GANs covering a variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cognit Sci 9(1):147–169

    Article  Google Scholar 

  2. Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3908–3916

  3. Antipov G, Baccouche M, Dugelay JL (2017) Face aging with conditional generative adversarial networks. In: 2017 IEEE international conference on image processing (ICIP), pp 2089–2093. IEEE

  4. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan. arXiv preprint arXiv:1701.07875

  5. Arora S, Ge R, Liang Y, Ma T, Zhang Y (2017) Generalization and equilibrium in generative adversarial nets (gans). In: Proceedings of the 34th international conference on machine learning, vol 70, pp 224–232. JMLR. org

  6. Bansal A, Ma S, Ramanan D, Sheikh Y (2018) Recycle-gan: Unsupervised video retargeting. In: Proceedings of the European conference on computer vision (ECCV), pp 119–135

  7. Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image inpainting. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co, pp 417–424

  8. Berthelot D, Schumm T, Metz L (2017) Began: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717

  9. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3722–3731

  10. Brock A, Donahue J, Simonyan K (2018) Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096

  11. Bulat A, Yang J, Tzimiropoulos G (2018) To learn image super-resolution, use a gan to learn how to do image degradation first. In: Proceedings of the European conference on computer vision (ECCV), pp 185–200

  12. Burt PJ, Adelson EH (1983) A multiresolution spline with application to image mosaics. ACM Trans Graph 2(4):217–236

    Article  Google Scholar 

  13. Che T, Li Y, Jacob AP, Bengio Y, Li W (2016) Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136

  14. Chen BC, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision, Springer, Berlin, pp 768–783

  15. Chen D, Xu D, Li H, Sebe N, Wang X (2018) Group consistent similarity learning via deep crf for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8649–8658

  16. Chen D, Yuan Z, Chen B, Zheng N (2016) Similarity learning with spatial constraints for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1268–1277

  17. 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

  18. Chen Y, Lai YK, Liu YJ (2018) Cartoongan: Generative adversarial networks for photo cartoonization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9465–9474

  19. Chongxuan L, Xu T, Zhu J, Zhang B (2017) Triple generative adversarial nets. In: Advances in neural information processing systems, pp 4088–4098

  20. Dai P, Ji R, Wang H, Wu Q, Huang Y (2018) Cross-modality person re-identification with generative adversarial training. In: IJCAI, pp 677–683

  21. Dash A, Gamboa JCB, Ahmed S, Liwicki M, Afzal MZ (2017) Tac-gan-text conditioned auxiliary classifier generative adversarial network. arXiv preprint arXiv:1703.06412

  22. Deng W, Zheng L, Ye Q, Kang G, Yang Y, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 994–1003

  23. Denton EL, Chintala S, Fergus R, et al (2015) Deep generative image models using a laplacian pyramid of adversarial networks. In: Advances in neural information processing systems, pp 1486–1494

  24. Doersch C (2016) Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908

  25. Donahue J, Krahenbuhl P, Darrell T (2016) Adversarial feature learning. arXiv preprint arXiv:1605.09782

  26. Dumoulin V, Belghazi I, Poole B, Mastropietro O, Lamb A, Arjovsky M, Courville A (2016) Adversarially learned inference. arXiv preprint arXiv:1606.00704

  27. Ehsani K, Mottaghi R, Farhadi A (2018) Segan: segmenting and generating the invisible. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6144–6153

  28. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vision 88(2):303–338

    Article  Google Scholar 

  29. Fabbri M, Calderara S, Cucchiara R (2017) Generative adversarial models for people attribute recognition in surveillance. In: 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS), pp 1–6. IEEE

  30. Fedus W, Goodfellow I, Dai AM (2018) Maskgan: Better text generation via filling in the\_. arXiv preprint arXiv:1801.07736

  31. Fulgeri F, Fabbri M, Alletto S, Calderara S, Cucchiara R (2019) Can adversarial networks hallucinate occluded people with a plausible aspect? arXiv preprint arXiv:1901.08097

  32. Ge Y, Li Z, Zhao H, Yin G, Yi S, Wang X, et al (2018) Fd-gan: Pose-guided feature distilling gan for robust person re-identification. In: Advances in neural information processing systems, pp 1230–1241

  33. Ghosh A, Kulharia V, Namboodiri VP, Torr PH, Dokania PK (2018) Multi-agent diverse generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8513–8521

  34. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  35. Gracias N, Mahoor M, Negahdaripour S, Gleason A (2009) Fast image blending using watersheds and graph cuts. Image Vis Comput 27(5):597–607

    Article  Google Scholar 

  36. Guimaraes GL, Sanchez-Lengeling B, Outeiral C, Farias PLC, Aspuru-Guzik A (2017) Objective-reinforced generative adversarial networks (organ) for sequence generation models. arXiv preprint arXiv:1705.10843

  37. 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

  38. 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

  39. Hitawala S (2018) Comparative study on generative adversarial networks. arXiv preprint arXiv:1801.04271

  40. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  41. Hoffman J, Tzeng E, Park T, Zhu JY, Isola P, Saenko K, Efros AA, Darrell T (2017) Cycada: cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213

  42. Hong Y, Hwang U, Yoo J, Yoon S (2017) How generative adversarial networks and their variants work: an overview. arXiv preprint arXiv:1711.05914

  43. Hsu CC, Hwang HT, Wu YC, Tsao Y, Wang HM (2017) Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks. arXiv preprint arXiv:1704.00849

  44. Huang X, Li Y, Poursaeed O, Hopcroft J, Belongie S (2017) Stacked generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5077–5086

  45. Huang X, Liu MY, Belongie S, Kautz J (2018) Multimodal unsupervised image-to-image translation. In: Proceedings of the European conference on computer vision (ECCV), pp 172–189

  46. Im DJ, Kim CD, Jiang H, Memisevic R (2016) Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110

  47. 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

  48. Jiao J, Zheng WS, Wu A, Zhu X, Gong S (2018) Deep low-resolution person re-identification. In: Thirty-second AAAI conference on artificial intelligence

  49. Jin Y, Zhang J, Li M, Tian Y, Zhu H, Fang Z (2017) Towards the automatic anime characters creation with generative adversarial networks. arXiv preprint arXiv:1708.05509

  50. Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A (2017) The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8(7):10883

    Article  Google Scholar 

  51. Kancharagunta KB, Dubey SR (2019) Csgan: Cyclic-synthesized generative adversarial networks for image-to-image transformation. arXiv preprint arXiv:1901.03554

  52. Kaneko T, Hiramatsu K, Kashino K (2017) Generative attribute controller with conditional filtered generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6089–6098

  53. Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196

  54. Karras T, Laine S, Aila T (2018) A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948

  55. Kemelmacher-Shlizerman I, Suwajanakorn S, Seitz SM (2014) Illumination-aware age progression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3334–3341

  56. 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, vol 70, pp 1857–1865. JMLR. org

  57. Kouw WM (2018) An introduction to domain adaptation and transfer learning. arXiv preprint arXiv:1812.11806

  58. Krause J, Stark M, Deng J, Fei-Fei L (2013) 3d object representations for fine-grained categorization. In: Proceedings of the IEEE international conference on computer vision workshops, pp 554–561

  59. Larsen ABL, Sønderby SK, Larochelle H, Winther O (2015) Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300

  60. Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, et al (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

  61. Lee JD, Simchowitz M, Jordan MI, Recht B (2016) Gradient descent only converges to minimizers. In: Conference on learning theory, pp 1246–1257

  62. Lee Sg, Hwang U (2017) Seonwoo min, and sungroh yoon. a seqgan for polyphonic music generation. arXiv preprint arXiv:1710.11418

  63. Li C, Wand M (2016) Precomputed real-time texture synthesis with markovian generative adversarial networks. In: European conference on computer vision, Springer, Berlin, pp 702–716

  64. Li D, Chen X, Zhang Z, Huang K (2017) Learning deep context-aware features over body and latent parts for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 384–393

  65. Li D, Zhang Z, Chen X, Ling H, Huang K (2016) A richly annotated dataset for pedestrian attribute recognition. arXiv preprint arXiv:1603.07054

  66. Li J, Liang X, Wei Y, Xu T, Feng J, Yan S (2017) Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1222–1230

  67. Li M, Huang H, Ma L, Liu W, Zhang T, Jiang Y (2018) Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks. In: Proceedings of the European conference on computer vision (ECCV), pp 184–199

  68. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: Deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152–159

  69. Liu AH, Liu YC, Yeh YY, Wang YCF (2018) A unified feature disentangler for multi-domain image translation and manipulation. In: Advances in neural information processing systems, pp 2590–2599

  70. Liu J (2018) Identity preserving generative adversarial network for cross-domain person re-identification. arXiv preprint arXiv:1811.11510

  71. Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Advances in neural information processing systems, pp 469–477

  72. Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730–3738

  73. Lv J, Wang X (2018) Cross-dataset person re-identification using similarity preserved generative adversarial networks. In: International conference on knowledge science, engineering and management, Springer, Berlin, pp 171–183

  74. Ma L, Jia X, Georgoulis S, Tuytelaars T, Van Gool L (2018) Exemplar guided unsupervised image-to-image translation. arXiv preprint arXiv:1805.11145

  75. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. arXiv preprint arXiv:1511.05644

  76. Mathieu M, Couprie C, LeCun Y (2015) Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440

  77. Medicine I (2019) Artificial intelligence for drug discovery, biomarker development and aging research. URL https://insilico.com/

  78. Mescheder L, Nowozin S, Geiger A (2017) The numerics of gans. In: Advances in neural information processing systems, pp 1825–1835

  79. Metz L, Poole B, Pfau D, Sohl-Dickstein J (2016) Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163

  80. Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784

  81. Miyato T, Koyama M (2018) cGANs with projection discriminator. arXiv preprint arXiv:1802.05637

  82. Mogren O (2016) C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904

  83. Nazeri K, Ng E, Joseph T, Qureshi F, Ebrahimi M (2019) Edgeconnect: generative image inpainting with adversarial edge learning. arXiv preprint arXiv:1901.00212

  84. Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011

  85. Nguyen A, Clune J, Bengio Y, Dosovitskiy A, Yosinski J (2017) Plug and play generative networks: conditional iterative generation of images in latent space. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4467–4477

  86. Nguyen A, Dosovitskiy A, Yosinski J, Brox T, Clune J (2016) Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Advances in neural information processing systems, pp 3387–3395

  87. Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th international conference on machine learning, vol 70, pp. 2642–2651. JMLR. org

  88. Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. arXiv preprint arXiv:1609.03499

  89. Oord A, Kalchbrenner N, Kavukcuoglu K (2016) Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759

  90. Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA (2016) Context encoders: Feature learning by inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2536–2544

  91. Qian X, Fu Y, Xiang T, Wang W, Qiu J, Wu Y, Jiang YG, Xue X (2018) Pose-normalized image generation for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 650–667

  92. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434

  93. Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396

  94. Reed SE, Akata Z, Mohan S, Tenka S, Schiele B, Lee H (2016) Learning what and where to draw. In: Advances in neural information processing systems, pp 217–225

  95. Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision, Springer, Berlin, pp 17–35

  96. 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

  97. Shen Y, Li H, Xiao T, Yi S, Chen D, Wang X (2018) Deep group-shuffling random walk for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2265–2274

  98. Shen Y, Li H, Yi S, Chen D, Wang X (2018) Person re-identification with deep similarity-guided graph neural network. In: Proceedings of the European conference on computer vision (ECCV), pp 486–504

  99. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R (2017) Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2107–2116

  100. Skansi S (2018) Autoencoders. In: Introduction to deep learning. Springer, Berlin, pp 153–163

  101. Springenberg JT (2015) Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:1511.06390

  102. Spurr A, Aksan E, Hilliges O (2017) Guiding infogan with semi-supervision. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, pp 119–134

  103. Su C, Li J, Zhang S, Xing J, Gao W, Tian Q (2017) Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3960–3969

  104. Taigman Y, Polyak A, Wolf L (2016) Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200

  105. Theis L, Oord Avd, Bethge M (2015) A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844

  106. Tian Y, Peng X, Zhao L, Zhang S, Metaxas DN (2018) Cr-gan: learning complete representations for multi-view generation. arXiv preprint arXiv:1806.11191

  107. Tolstikhin IO, Gelly S, Bousquet O, Simon-Gabriel CJ, Scholkopf B (2017) Adagan: boosting generative models. In: Advances in neural information processing systems, pp 5424–5433

  108. Tran L, Yin X, Liu X (2017) Disentangled representation learning gan for pose-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1415–1424

  109. 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

  110. Uyttendaele M, Eden A, Skeliski R (2001) Eliminating ghosting and exposure artifacts in image mosaics. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol 2, pp II–II. IEEE

  111. Varior RR, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: European conference on computer vision. Springer, Berlin, pp 791–808

  112. Vondrick C, Pirsiavash H, Torralba A (2016) Generating videos with scene dynamics. In: Advances in neural information processing systems, pp 613–621

  113. Vougioukas K, Petridis S, Pantic M (2018) End-to-end speech-driven facial animation with temporal gans. arXiv preprint arXiv:1805.09313

  114. 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

  115. Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Change Loy C (2018) Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV), pp 0–0

  116. Wang X, Zheng WS, Li X, Zhang J (2016) Cross-scenario transfer person reidentification. IEEE Trans Circuits Syst Video Technol 26(8):1447–1460

    Article  Google Scholar 

  117. Wang Y, Wu L (2018) Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw 103:1–8

    Article  Google Scholar 

  118. Wang Y, Wu L, Lin X, Gao J (2018) Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans Neural Netw Learn Syst 99:1–11

    Google Scholar 

  119. Wang Z, Ye M, Yang F, Bai X, Satoh S (2018) Cascaded SR-GAN for scale-adaptive low resolution person re-identification. In: IJCAI, pp 3891–3897

  120. Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 79–88

  121. Wu A, Zheng WS, Yu HX, Gong S, Lai J (2017) Rgb-infrared cross-modality person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 5380–5389

  122. Wu D, Zhang K, Zheng SJ, Huang DS (2018) Random occlusion-recovery for person re-identification. arXiv preprint arXiv:1809.09970

  123. Wu H, Zheng S, Zhang J, Huang K (2017) Gp-gan: Towards realistic high-resolution image blending. arXiv preprint arXiv:1703.07195

  124. Wu L, Wang Y, Gao J, Li X (2018) Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn 73:275–288

    Article  Google Scholar 

  125. Xiao T, Hong J, Ma J (2018) Elegant: Exchanging latent encodings with gan for transferring multiple face attributes. In: Proceedings of the European conference on computer vision (ECCV), pp 168–184

  126. Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) SeGAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16(3–4):383–392

    Article  Google Scholar 

  127. Yan X, Yang J, Sohn K, Lee H (2016) Attribute2image: conditional image generation from visual attributes. In: European conference on computer vision. Springer, Berlin, pp 776–791

  128. Yang D, Xiong T, Xu D, Huang Q, Liu D, Zhou SK, Xu Z, Park J, Chen M, Tran TD et al (2017) Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization. In: International conference on information processing in medical imaging. Springer, Berlin, pp 633–644

  129. Yeh RA, Chen C, Yian Lim T, Schwing AG, Hasegawa-Johnson M, Do MN (2017) Semantic image inpainting with deep generative models. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5485–5493

  130. Yin W, Fu Y, Sigal L, Xue X (2017) Semi-latent gan: Learning to generate and modify facial images from attributes. arXiv preprint arXiv:1704.02166

  131. Yu HX, Wu A, Zheng WS (2017) Cross-view asymmetric metric learning for unsupervised person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 994–1002

  132. 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

  133. 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

  134. Yu L, Zhang W, Wang J, Yu Y (2017) Seqgan: Sequence generative adversarial nets with policy gradient. In: Thirty-first AAAI conference on artificial intelligence

  135. Yu X, Cai X, Ying Z, Li T, Li G (2018) Singlegan: Image-to-image translation by a single-generator network using multiple generative adversarial learning. arXiv preprint arXiv:1810.04991

  136. Zhang H, Goodfellow I, Metaxas D, Odena A (2018) Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318

  137. Zhang J, Shu Y, Xu S, Cao G, Zhong F, Qin X (2018) Sparsely grouped multi-task generative adversarial networks for facial attribute manipulation. arXiv preprint arXiv:1805.07509

  138. Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1239–1248

  139. Zhang M, Teck Ma K, Hwee Lim J, Zhao Q, Feng J (2017) Deep future gaze: gaze anticipation on egocentric videos using adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4372–4381

  140. Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5810–5818

  141. Zhao B, Chang B, Jie Z, Sigal L (2018) Modular generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV), pp 150–165

  142. Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, Wang X, Tang X (2017) Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1077–1085

  143. Zhao J, Mathieu M, LeCun Y (2016) Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126

  144. Zhao L, Li X, Zhuang Y, Wang J (2017) Deeply-learned part-aligned representations for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3219–3228

  145. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124

  146. Zhou S, Xiao T, Yang Y, Feng D, He Q, He W (2017) Genegan: Learning object transfiguration and attribute subspace from unpaired data. arXiv preprint arXiv:1705.04932

  147. 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

  148. Zhu Z, Liang D, Zhang S, Huang X, Li B, Hu S (2016) Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2110–2118

Download references

Author information

Authors and Affiliations

  1. King Khalid University, Abha, Saudi Arabia

    Hamed Alqahtani

  2. Macquarie University, Sydney, Australia

    Manolya Kavakli-Thorne

  3. SBSSTC, Ferozepur, Punjab, India

    Gulshan Kumar

Authors
  1. Hamed Alqahtani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manolya Kavakli-Thorne
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gulshan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamed Alqahtani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alqahtani, H., Kavakli-Thorne, M. & Kumar, G. Applications of Generative Adversarial Networks (GANs): An Updated Review. Arch Computat Methods Eng 28, 525–552 (2021). https://doi.org/10.1007/s11831-019-09388-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-019-09388-y

Keywords

Search

Navigation