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We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors.
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
Animesh Karnewar and Oliver Wang. 2020 · 1903
Earlier work this paper cites.
Expression Conditional GAN for Facial Expression-to-Expression Translation
Hao Tang, Wei Wang, Songsong Wu, Xinya Chen, Dan Xu, Nicu Sebe, and Yan Yan. 2019 · 1905
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Adversarial Video Generation on Complex Datasets
Aidan Clark, Jeff Donahue, and Karen Simonyan. 2019 · 1907
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Galaxy Image Simulation Using Progressive GANs
Mohamad Dia, Elodie Savary, Martin Melchior, and Frederic Courbin. 2019 · 1909
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DermGAN: Synthetic Generation of Clinical Skin Images with Pathology
Amirata Gohorbani, Vivek Natarajan, David Devoud Coz, and Yuan Liu. 2019 · 1911
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Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy
J. G. Fujimoto, C. Pitris, S. A. Boppart, and M. E. Brezinski. 2000 · 2000
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A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, and Jieping Ye. 2020 · 2001
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. 2004 · 2003
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Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003 , Vol. 2. 1398–1402 Vol.2
Z. Wang, E.P. Simoncelli, and A.C. Bovik. 2003 · 2003
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Garment Design with Generative Adversarial Networks
Chenxi Yuan and Mohsen Moghaddam. 2020 · 2007
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A kernel method for the two-sample problem
Arthur Gretton, Karsten Borgwardt, Malte J Rasch, Bernhard Scholkopf, and Alexander J Smola. 2008 · 2008
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ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition . 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
Heng Pan. 2020 · 2009
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Optimal transport: old and new . Vol. 338
Cédric Villani. 2009 · 2009
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Boltzmann Machines
Geoffrey Hinton. 2010 · 2010
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Kullback-Leibler Divergence
James M. Joyce. 2011 · 2011
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Encoding large scale cosmological structure with Generative Adversarial Networks
Marion Ullmo, Aurélien Decelle, and Nabila Aghanim. 2020 · 2011
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Generating Synthetic Multispectral Satellite Imagery from Sentinel-2
Tharun Mohandoss, Aditya Kulkarni, Daniel Northrup, Ernest Mwebaze, and Hamed Alemohammad. 2020 · 2012
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Generative Adversarial Nets. In Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger (Eds.), Vol. 27. Curran Associates, Inc., 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero. 2014 · 2014
Earlier work this paper cites.
Sequence to Sequence Learning with Neural Networks. In NIPS
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
Image Super-Resolution Using Deep Convolutional Networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2016a · 2015
Earlier work this paper cites.
Image Super-Resolution Using Deep Convolutional Networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2016b · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015a · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 , Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi (Eds.). Springer International Publishing, Cham, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Earlier work this paper cites.
Learning Spatiotemporal Features with 3D Convolutional Networks. In 2015 IEEE International Conference on Computer Vision (ICCV) . 4489–4497
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. 2015 · 2015
Earlier work this paper cites.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Emily Denton, Sam Gross, and Rob Fergus. 2016 · 2016
Earlier work this paper cites.
Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2016 · 2016
Earlier work this paper cites.
Accurate Image Super-Resolution Using Very Deep Convolutional Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 1646–1654
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016a · 2016
Earlier work this paper cites.
Deeply-Recursive Convolutional Network for Image Super-Resolution. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 1637–1645
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016b · 2016
Earlier work this paper cites.
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
Matt J. Kusner and José Miguel Hernández-Lobato. 2016 · 2016
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun. 2016 · 2016
Earlier work this paper cites.
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Olof Mogren. 2016 · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Invertible Conditional GANs for image editing
Guim Perarnau, Joost van de Weijer, Bogdan Raducanu, and Jose M. Álvarez. 2016 · 2016
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, and Soumith Chintala. 2016 · 2016
Earlier work this paper cites.
Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. 2016 · 2016
Earlier work this paper cites.
Rethinking the Inception Architecture for Computer Vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2818–2826
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
WaveNet: A Generative Model for Raw Audio. In Arxiv
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alexander Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Generating Videos with Scene Dynamics. In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS’16) . Curran Associates Inc., Red Hook, NY, USA, 613–621
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. 2016 · 2016
Earlier work this paper cites.
Towards Principled Methods for Training Generative Adversarial Networks
Martin Arjovsky and Léon Bottou. 2017 · 2017
Earlier work this paper cites.
Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70) , Doina Precup and Yee Whye Teh (Eds.). PMLR, 214–223
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
Earlier work this paper cites.
BEGAN: Boundary Equilibrium Generative Adversarial Networks
David Berthelot, Thomas Schumm, and Luke Metz. 2017 · 2017
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Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Prateep Bhattacharjee and Sukhendu Das. 2017 · 2017
Earlier work this paper cites.
Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li. 2017 · 2017
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Learning to Fuse Music Genres with Generative Adversarial Dual Learning. In 2017 IEEE International Conference on Data Mining (ICDM) . 817–822
Zhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu, Alexander Lerch, and Chang-Tien Lu. 2017 · 2017
Earlier work this paper cites.
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2017 · 2017
Earlier work this paper cites.
End-to-End Adversarial Retinal Image Synthesis
P. Costa, A. Galdran, M. I. Meyer, M. Niemeijer, M. Abràmoff, A. M. Mendonça, and A. Campilho. 2018 · 2017
Earlier work this paper cites.
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang. 2017 · 2017
Earlier work this paper cites.
Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch. 2017 · 2017
Earlier work this paper cites.
CytoGAN: Generative Modeling of Cell Images
Peter Goldsborough, Nick Pawlowski, Juan C Caicedo, Shantanu Singh, and Anne E Carpenter. 2017 · 2017
Earlier work this paper cites.
Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017b · 2017
Earlier work this paper cites.
DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4941–4949
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R. Venkatesh Babu. 2017 · 2017
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Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV) . 2980–2988
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017 · 2017
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Curran Associates Inc., Red Hook, NY, USA, 6629–6640
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2261–2269
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. 2017 · 2017
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Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization. In 2017 IEEE International Conference on Computer Vision (ICCV) . 1510–1519
Xun Huang and Serge Belongie. 2017 · 2017
Earlier work this paper cites.
Image-to-Image Translation with Conditional Adversarial Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 5967–5976
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017 · 2017
Earlier work this paper cites.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017 · 2017
Earlier work this paper cites.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, and Jiwon Kim. 2017 · 2017
Earlier work this paper cites.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi. 2017 · 2017
Earlier work this paper cites.
Yitong Li, Martin Renqiang Min, Dinghan Shen, David Carlson, and Lawrence Carin. 2017 · 2017
Earlier work this paper cites.
Dual Motion GAN for Future-Flow Embedded Video Prediction. In 2017 IEEE International Conference on Computer Vision (ICCV) . 1762–1770
Xiaodan Liang, Lisa Lee, Wei Dai, and Eric P. Xing. 2017 · 2017
Cited alongside, same era.
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
Daoyu Lin, Kun Fu, Yang Wang, Guangluan Xu, and Xian Sun. 2017 · 2017
Cited alongside, same era.
Flexible Spatio-Temporal Networks for Video Prediction. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2137–2145
Chaochao Lu, Michael Hirsch, and Bernhard Schölkopf. 2017 · 2017
Cited alongside, same era.
Pose Guided Person Image Generation. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, and Luc Van Gool. 2017 · 2017
Cited alongside, same era.
Predicting Future Frames Using Retrospective Cycle GAN. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 1811–1820
Yong-Hoon Kwon and Min-Gyu Park. 2019 · 2019
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Using GAN to Generate Sport News from Live Game Stats. In Cognitive Computing – ICCC 2019 , Ruifeng Xu, Jianzong Wang, and Liang-Jie Zhang (Eds.). Springer International Publishing, Cham, 102–116
Changliang Li, Yixin Su, Ji Qi, and Min Xiao. 2019b · 2019
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Fine-Grained Visual Dribbling Style Analysis for Soccer Videos With Augmented Dribble Energy Image. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . 2439–2447
Runze Li and Bir Bhanu. 2019 · 2019
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StoryGAN: A Sequential Conditional GAN for Story Visualization. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 6322–6331
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. 2019a · 2019
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Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, and Stephen Paul Smolley. 2017 · 2017
Cited alongside, same era.
Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. 2017 · 2017
Cited alongside, same era.
Conditional Image Synthesis with Auxiliary Classifier GANs. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70) , Doina Precup and Yee Whye Teh (Eds.). PMLR, 2642–2651
Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017 · 2017
Cited alongside, same era.
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
Katsunori Ohnishi, Shohei Yamamoto, Yoshitaka Ushiku, and Tatsuya Harada. 2017 · 2017
Cited alongside, same era.
GANs for Biological Image Synthesis. In ICCV 2017 - IEEE International Conference on Computer Vision . Venice, Italy
Anton Osokin, Anatole Chessel, Rafael E. Carazo Salas, and Federico Vaggi. 2017 · 2017
Cited alongside, same era.
Stabilizing Training of Generative Adversarial Networks through Regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. 2017 · 2017
Cited alongside, same era.
Temporal Generative Adversarial Nets with Singular Value Clipping
Masaki Saito, Eiichi Matsumoto, and Shunta Saito. 2017 · 2017
Cited alongside, same era.
Generating the Future with Adversarial Transformers. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2992–3000
Carl Vondrick and Antonio Torralba. 2017 · 2017
Cited alongside, same era.
Toward AI fashion design: An Attribute-GAN model for clothing match
Linlin Liu, Haijun Zhang, Yuzhu Ji, and Q.M. Jonathan Wu. 2019 · 2019
Later among the works it cites.
Synthesis of Medical Images Using GANs. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures , Hayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, and Miguel Ángel González Ballester (Eds.). Springer International Publishing, Cham, 125–134
Luise Middel, Christoph Palm, and Marius Erdt. 2019 · 2019
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CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
Asma Nouira, Nataliya Sokolovska, and Jean-Claude Crivello. 2019 · 2019
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Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Veit Sandfort, Ke Yan, Perry J. Pickhardt, and Ronald M. Summers. 2019 · 2019
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Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning
Michael J Smith and James E Geach. 2019 · 2019
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Unsupervised Person Image Generation With Semantic Parsing Transformation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Sijie Song, Wei Zhang, Jiaying Liu, and Tao Mei. 2019 · 2019
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Modeling financial time-series with generative adversarial networks
Shuntaro Takahashi, Yu Chen, and Kumiko Tanaka-Ishii. 2019 · 2019
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End-to-End Speech-Driven Realistic Facial Animation with Temporal GANs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. 2019 · 2019
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Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019 · 2019
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Retinal image synthesis from multiple-landmarks input with generative adversarial networks
Zekuan Yu, Qing Xiang, Jiahao Meng, Caixia Kou, Qiushi Ren, and Yanye Lu. 2019 · 2019
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Image De-Raining Using a Conditional Generative Adversarial Network
He Zhang, Vishwanath Sindagi, and Vishal M. Patel. 2020b · 2019
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Realistic Face Image Generation Based on Generative Adversarial Network. In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing . 303–306
TING ZHANG, WEN-HONG TIAN, TING-YING ZHENG, ZU-NING LI, XUE-MEI DU, and FAN LI. 2019 · 2019
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High-quality face image generation based on generative adversarial networks
Zhixin Zhang, Xuhua Pan, Shuhao Jiang, and Peijun Zhao. 2020a · 2019
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GANsDTA: Predicting Drug-Target Binding Affinity Using GANs
Lingling Zhao, Junjie Wang, Long Pang, Yang Liu, and Jun Zhang. 2020 · 2019
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Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, and Xiaogang Wang. 2019 · 2019
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Generative adversarial network: An overview of theory and applications
Alankrita Aggarwal, Mamta Mittal, and Gopi Battineni. 2021 · 2020
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Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks
Tileli Amimeur, Jeremy M. Shaver, Randal R. Ketchem, J. Alex Taylor, Rutilio H. Clark, Josh Smith, Danielle Van Citters, Christine C. Siska, Pauline Smidt, Megan Sprague, Bruce A. Kerwin, and Dean Pettit. 2020 · 2020
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Scripted Video Generation With a Bottom-Up Generative Adversarial Network
Qi Chen, Qi Wu, Jian Chen, Qingyao Wu, Anton van den Hengel, and Mingkui Tan. 2020 · 2020
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Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, and Jianjun Hu. 2020 · 2020
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Using generative adversarial networks to synthesize artificial financial datasets
Dmitry Efimov, Di Xu, Luyang Kong, Alexey Nefedov, and Archana Anandakrishnan. 2020 · 2020
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A comprehensive survey and analysis of generative models in machine learning
Harshvardhan GM, Mahendra Kumar Gourisaria, Manjusha Pandey, and Siddharth Swarup Rautaray. 2020 · 2020
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MedSRGAN: medical images super-resolution using generative adversarial networks
Yuchong Gu, Zitao Zeng, Haibin Chen, Jun Wei, Yaqin Zhang, Binghui Chen, Yingqin Li, Yujuan Qin, Qing Xie, Zhuoren Jiang, and Yao Lu. 2020 · 2020
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Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks
Anna Hu, Zhong Xie, Yongyang Xu, Mingyu Xie, Liang Wu, and Qinjun Qiu. 2020b · 2020
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Learning Representations of Inorganic Materials from Generative Adversarial Networks
Tiantian Hu, Hui Song, Tao Jiang, and Shaobo Li. 2020a · 2020
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Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
Yuning Jiang and Jinhua Li. 2020 · 2020
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SpanBERT: Improving Pre-training by Representing and Predicting Spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Analyzing and Improving the Image Quality of StyleGAN. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020 · 2020
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TiVGAN: Text to Image to Video Generation With Step-by-Step Evolutionary Generator
Doyeon Kim, Donggyu Joo, and Junmo Kim. 2020a · 2020
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Generative Adversarial Networks for Crystal Structure Prediction
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, and Yousung Jung. 2020b · 2020
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Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion
Jun Li, Zhaocong Wu, Zhongwen Hu, Jiaqi Zhang, Mingliang Li, Lu Mo, and Matthieu Molinier. 2020b · 2020
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Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion
Jiayi Ma, Wei Yu, Chen Chen, Pengwei Liang, Xiaojie Guo, and Junjun Jiang. 2020 · 2020
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Designing complex architectured materials with generative adversarial networks
Yunwei Mao, Qi He, and Xuanhe Zhao. 2020 · 2020
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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, and Stefan Bonn. 2020 · 2020
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fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy
Tomoyuki Nagasawa, Takanori Sato, Isao Nambu, and Yasuhiro Wada. 2020 · 2020
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A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer’s disease
Jinhee Park, Hyerin Kim, Jaekwang Kim, and Mookyung Cheon. 2020 · 2020
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Review on Generative Adversarial Networks. In 2020 International Conference on Communication and Signal Processing (ICCSP) . 0479–0482
Vishnu B. Raj and K Hareesh. 2020 · 2020
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HRPGAN: A GAN-based Model to Generate High-resolution Remote Sensing Images
Hai Sun, Ping Wang, Yifan Chang, Li Qi, Hailei Wang, Dan Xiao, Cheng Zhong, Xuelian Wu, Wenbo Li, and Bingyu Sun. 2020 · 2020
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Remote Sensing Images Dehazing Algorithm based on Cascade Generative Adversarial Networks. In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) . 316–321
Xiao Sun and Jindong Xu. 2020 · 2020
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An Automated System for Generating Tactical Performance Statistics for Individual Soccer Players From Videos
Rajkumar Theagarajan and Bir Bhanu. 2021 · 2020
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N. Tokui. 2020 · 2020
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Quant gans: Deep generation of financial time series
Magnus Wiese, Robert Knobloch, Ralf Korn, and Peter Kretschmer. 2020 · 2020
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Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra
Minglei Wu, Yude Bu, Jingchang Pan, Zhenping Yi, and Xiaoming Kong. 2020 · 2020
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scIGANs: single-cell RNA-seq imputation using generative adversarial networks
Yungang Xu, Zhigang Zhang, Lei You, Jiajia Liu, Zhiwei Fan, and Xiaobo Zhou. 2020 · 2020
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Medical Image Enhancement Using Super Resolution Methods. In Computational Science – ICCS 2020 , Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, and João Teixeira (Eds.). Springer International Publishing, Cham, 496–508
Koki Yamashita and Konstantin Markov. 2020 · 2020
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GAN-Based Image Super-Resolution with a Novel Quality Loss
Xining Zhu, Lin Zhang, Lijun Zhang, Xiao Liu, Ying Shen, and Shengjie Zhao. 2020 · 2020
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Applications of Generative Adversarial Networks (GANs): An Updated Review
Hamed Alqahtani, Manolya Kavakli-Thorne, and Gulshan Kumar. 2021 · 2021
Closest in time.
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks
Bekir Z. Demiray, Muhammed Sit, and Ibrahim Demir. 2021 · 2021
Closest in time.
Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN
ChangHyuk Kwon, Sangjin Park, Soohyun Ko, and Jaegyoon Ahn. 2021 · 2021
Closest in time.
Virtual microstructure design for steels using generative adversarial networks
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Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
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Generation of Solar UV and EUV Images from SDO/HMI Magnetograms by Deep Learning
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