Generative adversarial network (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Generative adversarial network" in English language version.

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Zhirui Zhang; Shujie Liu; Mu Li; Ming Zhou; Enhong Chen (October 2018). "Bidirectional Generative Adversarial Networks for Neural Machine Translation" (PDF). pp. 190–199.

acm.org

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Lin, Zinan; et al. (December 2018). PacGAN: the power of two samples in generative adversarial networks. 32nd International Conference on Neural Information Processing Systems. pp. 1505–1514. arXiv:1712.04086.
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adweek.com

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aps.org

link.aps.org

Nista, Ludovico; Pitsch, Heinz; Schumann, Christoph D. K.; Bode, Mathis; Grenga, Temistocle; MacArt, Jonathan F.; Attili, Antonio (June 4, 2024). "Influence of adversarial training on super-resolution turbulence reconstruction". Physical Review Fluids. 9 (6): 064601. arXiv:2308.16015. Bibcode:2024PhRvF...9f4601N. doi:10.1103/PhysRevFluids.9.064601.

arcadeattack.co.uk

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arxiv.org

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Ho, Jonathon; Ermon, Stefano (2016). "Generative Adversarial Imitation Learning". Advances in Neural Information Processing Systems. 29: 4565–4573. arXiv:1606.03476.
Luc, Pauline; Couprie, Camille; Chintala, Soumith; Verbeek, Jakob (November 25, 2016). "Semantic Segmentation using Adversarial Networks". NIPS Workshop on Adversarial Training, Dec, Barcelona, Spain. 2016. arXiv:1611.08408.
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Goodfellow, Ian (April 3, 2017). "NIPS 2016 Tutorial: Generative Adversarial Networks". arXiv:1701.00160 [cs.LG].
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Weng, Lilian (April 18, 2019). "From GAN to WGAN". arXiv:1904.08994 [cs.LG].
Karras, Tero; Aila, Timo; Laine, Samuli; Lehtinen, Jaakko (October 1, 2017). "Progressive Growing of GANs for Improved Quality, Stability, and Variation". arXiv:1710.10196 [cs.NE].
Soviany, Petru; Ardei, Claudiu; Ionescu, Radu Tudor; Leordeanu, Marius (October 22, 2019). "Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)". arXiv:1910.08967 [cs.LG].
Hacohen, Guy; Weinshall, Daphna (May 24, 2019). "On The Power of Curriculum Learning in Training Deep Networks". International Conference on Machine Learning. PMLR: 2535–2544. arXiv:1904.03626.
Lin, Zinan; et al. (December 2018). PacGAN: the power of two samples in generative adversarial networks. 32nd International Conference on Neural Information Processing Systems. pp. 1505–1514. arXiv:1712.04086.
Mescheder, Lars; Geiger, Andreas; Nowozin, Sebastian (July 31, 2018). "Which Training Methods for GANs do actually Converge?". arXiv:1801.04406 [cs.LG].
Brock, Andrew; Donahue, Jeff; Simonyan, Karen (September 1, 2018). Large Scale GAN Training for High Fidelity Natural Image Synthesis. International Conference on Learning Representations 2019. arXiv:1809.11096.
Heusel, Martin; Ramsauer, Hubert; Unterthiner, Thomas; Nessler, Bernhard; Hochreiter, Sepp (2017). "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium". Advances in Neural Information Processing Systems. 30. Curran Associates, Inc. arXiv:1706.08500.
Kingma, Diederik P.; Ba, Jimmy (January 29, 2017). "Adam: A Method for Stochastic Optimization". arXiv:1412.6980 [cs.LG].
Zhang, Richard; Isola, Phillip; Efros, Alexei A.; Shechtman, Eli; Wang, Oliver (2018). "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric". pp. 586–595. arXiv:1801.03924 [cs.CV].
Borji, Ali (February 1, 2019). "Pros and cons of GAN evaluation measures". Computer Vision and Image Understanding. 179: 41–65. arXiv:1802.03446. doi:10.1016/j.cviu.2018.10.009. ISSN 1077-3142. S2CID 3627712.
Odena, Augustus; Olah, Christopher; Shlens, Jonathon (July 17, 2017). "Conditional Image Synthesis with Auxiliary Classifier GANs". International Conference on Machine Learning. PMLR: 2642–2651. arXiv:1610.09585.
Larsen, Anders Boesen Lindbo; Sønderby, Søren Kaae; Larochelle, Hugo; Winther, Ole (June 11, 2016). "Autoencoding beyond pixels using a learned similarity metric". International Conference on Machine Learning. PMLR: 1558–1566. arXiv:1512.09300.
Jiang, Yifan; Chang, Shiyu; Wang, Zhangyang (December 8, 2021). "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up". arXiv:2102.07074 [cs.CV].
Grover, Aditya; Dhar, Manik; Ermon, Stefano (May 1, 2017). "Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models". arXiv:1705.08868 [cs.LG].
Arjovsky, Martin; Bottou, Léon (January 1, 2017). "Towards Principled Methods for Training Generative Adversarial Networks". arXiv:1701.04862 [stat.ML].
Goodfellow, Ian J. (December 1, 2014). "On distinguishability criteria for estimating generative models". arXiv:1412.6515 [stat.ML].
Lim, Jae Hyun; Ye, Jong Chul (May 8, 2017). "Geometric GAN". arXiv:1705.02894 [stat.ML].
Mao, Xudong; Li, Qing; Xie, Haoran; Lau, Raymond Y. K.; Wang, Zhen; Paul Smolley, Stephen (2017). "Least Squares Generative Adversarial Networks". 2017 IEEE International Conference on Computer Vision (ICCV). pp. 2794–2802. arXiv:1611.04076. doi:10.1109/ICCV.2017.304. ISBN 978-1-5386-1032-9.
Makhzani, Alireza; Shlens, Jonathon; Jaitly, Navdeep; Goodfellow, Ian; Frey, Brendan (2016). "Adversarial Autoencoders". arXiv:1511.05644 [cs.LG].
Chen, Xi; Duan, Yan; Houthooft, Rein; Schulman, John; Sutskever, Ilya; Abbeel, Pieter (2016). "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets". Advances in Neural Information Processing Systems. 29. Curran Associates, Inc. arXiv:1606.03657.
Donahue, Jeff; Krähenbühl, Philipp; Darrell, Trevor (2016). "Adversarial Feature Learning". arXiv:1605.09782 [cs.LG].
Dumoulin, Vincent; Belghazi, Ishmael; Poole, Ben; Mastropietro, Olivier; Arjovsky, Alex; Courville, Aaron (2016). "Adversarially Learned Inference". arXiv:1606.00704 [stat.ML].
Xi Chen; Yan Duan; Rein Houthooft; John Schulman; Ilya Sutskever; Pieter Abeel (2016). "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets". arXiv:1606.03657 [cs.LG].
Zhu, Jun-Yan; Park, Taesung; Isola, Phillip; Efros, Alexei A. (2017). "Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks". pp. 2223–2232. arXiv:1703.10593 [cs.CV].
Isola, Phillip; Zhu, Jun-Yan; Zhou, Tinghui; Efros, Alexei A. (2017). "Image-To-Image Translation With Conditional Adversarial Networks". pp. 1125–1134. arXiv:1611.07004 [cs.CV].
Shengyu, Zhao; Zhijian, Liu; Ji, Lin; Jun-Yan, Zhu; Song, Han (2020). "Differentiable Augmentation for Data-Efficient GAN Training". Advances in Neural Information Processing Systems. 33. arXiv:2006.10738.
Shaham, Tamar Rott; Dekel, Tali; Michaeli, Tomer (October 2019). "SinGAN: Learning a Generative Model from a Single Natural Image". 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE. pp. 4569–4579. arXiv:1905.01164. doi:10.1109/iccv.2019.00467. ISBN 978-1-7281-4803-8. S2CID 145052179.
Karras, Tero; Laine, Samuli; Aila, Timo (June 2019). "A Style-Based Generator Architecture for Generative Adversarial Networks". 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. pp. 4396–4405. arXiv:1812.04948. doi:10.1109/cvpr.2019.00453. ISBN 978-1-7281-3293-8. S2CID 54482423.
Karras, Tero; Laine, Samuli; Aittala, Miika; Hellsten, Janne; Lehtinen, Jaakko; Aila, Timo (June 2020). "Analyzing and Improving the Image Quality of StyleGAN". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. pp. 8107–8116. arXiv:1912.04958. doi:10.1109/cvpr42600.2020.00813. ISBN 978-1-7281-7168-5. S2CID 209202273.
Li, Bonnie; François-Lavet, Vincent; Doan, Thang; Pineau, Joelle (February 14, 2021). "Domain Adversarial Reinforcement Learning". arXiv:2102.07097 [cs.LG].
Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan (February 1, 2017). "Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit". Monthly Notices of the Royal Astronomical Society: Letters. 467 (1): L110 – L114. arXiv:1702.00403. Bibcode:2017MNRAS.467L.110S. doi:10.1093/mnrasl/slx008. S2CID 7213940.
Mustafa, Mustafa; Bard, Deborah; Bhimji, Wahid; Lukić, Zarija; Al-Rfou, Rami; Kratochvil, Jan M. (May 6, 2019). "CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks". Computational Astrophysics and Cosmology. 6 (1): 1. arXiv:1706.02390. Bibcode:2019ComAC...6....1M. doi:10.1186/s40668-019-0029-9. ISSN 2197-7909. S2CID 126034204.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin (2017). "Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis". Computing and Software for Big Science. 1 (1): 4. arXiv:1701.05927. Bibcode:2017CSBS....1....4D. doi:10.1007/s41781-017-0004-6. S2CID 88514467.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin (2018). "Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters". Physical Review Letters. 120 (4): 042003. arXiv:1705.02355. Bibcode:2018PhRvL.120d2003P. doi:10.1103/PhysRevLett.120.042003. PMID 29437460. S2CID 3330974.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin (2018). "CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks". Phys. Rev. D. 97 (1): 014021. arXiv:1712.10321. Bibcode:2018PhRvD..97a4021P. doi:10.1103/PhysRevD.97.014021. S2CID 41265836.
Erdmann, Martin; Glombitza, Jonas; Quast, Thorben (2019). "Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network". Computing and Software for Big Science. 3 (1): 4. arXiv:1807.01954. Bibcode:2019CSBS....3....4E. doi:10.1007/s41781-018-0019-7. S2CID 54216502.
Musella, Pasquale; Pandolfi, Francesco (2018). "Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks". Computing and Software for Big Science. 2 (1): 8. arXiv:1805.00850. Bibcode:2018CSBS....2....8M. doi:10.1007/s41781-018-0015-y. S2CID 119474793.
SHiP, Collaboration (2019). "Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks". Journal of Instrumentation. 14 (11): 11028. arXiv:1909.04451. Bibcode:2019JInst..14P1028A. doi:10.1088/1748-0221/14/11/P11028. S2CID 202542604.
Nista, Ludovico; Pitsch, Heinz; Schumann, Christoph D. K.; Bode, Mathis; Grenga, Temistocle; MacArt, Jonathan F.; Attili, Antonio (June 4, 2024). "Influence of adversarial training on super-resolution turbulence reconstruction". Physical Review Fluids. 9 (6): 064601. arXiv:2308.16015. Bibcode:2024PhRvF...9f4601N. doi:10.1103/PhysRevFluids.9.064601.
Fukami, Kai; Fukagata, Koji; Taira, Kunihiko (August 1, 2020). "Assessment of supervised machine learning methods for fluid flows". Theoretical and Computational Fluid Dynamics. 34 (4): 497–519. arXiv:2001.09618. Bibcode:2020ThCFD..34..497F. doi:10.1007/s00162-020-00518-y. ISSN 1432-2250.
Elgammal, Ahmed; Liu, Bingchen; Elhoseiny, Mohamed; Mazzone, Marian (2017). "CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms". arXiv:1706.07068 [cs.AI].
Tang, Xiaoou; Qiao, Yu; Loy, Chen Change; Dong, Chao; Liu, Yihao; Gu, Jinjin; Wu, Shixiang; Yu, Ke; Wang, Xintao (September 1, 2018). "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks". arXiv:1809.00219 [cs.CV].
Seung Wook Kim; Zhou, Yuhao; Philion, Jonah; Torralba, Antonio; Fidler, Sanja (2020). "Learning to Simulate Dynamic Environments with GameGAN". arXiv:2005.12126 [cs.CV].
Yu, Yi; Canales, Simon (2021). "Conditional LSTM-GAN for Melody Generation from Lyrics". ACM Transactions on Multimedia Computing, Communications, and Applications. 17: 1–20. arXiv:1908.05551. doi:10.1145/3424116. ISSN 1551-6857. S2CID 199668828.
Antipov, Grigory; Baccouche, Moez; Dugelay, Jean-Luc (2017). "Face Aging With Conditional Generative Adversarial Networks". arXiv:1702.01983 [cs.CV].
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columbia.edu

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courierpress.com

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doi.org

Borji, Ali (February 1, 2019). "Pros and cons of GAN evaluation measures". Computer Vision and Image Understanding. 179: 41–65. arXiv:1802.03446. doi:10.1016/j.cviu.2018.10.009. ISSN 1077-3142. S2CID 3627712.
Mao, Xudong; Li, Qing; Xie, Haoran; Lau, Raymond Y. K.; Wang, Zhen; Paul Smolley, Stephen (2017). "Least Squares Generative Adversarial Networks". 2017 IEEE International Conference on Computer Vision (ICCV). pp. 2794–2802. arXiv:1611.04076. doi:10.1109/ICCV.2017.304. ISBN 978-1-5386-1032-9.
Shaham, Tamar Rott; Dekel, Tali; Michaeli, Tomer (October 2019). "SinGAN: Learning a Generative Model from a Single Natural Image". 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE. pp. 4569–4579. arXiv:1905.01164. doi:10.1109/iccv.2019.00467. ISBN 978-1-7281-4803-8. S2CID 145052179.
Karras, Tero; Laine, Samuli; Aila, Timo (June 2019). "A Style-Based Generator Architecture for Generative Adversarial Networks". 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. pp. 4396–4405. arXiv:1812.04948. doi:10.1109/cvpr.2019.00453. ISBN 978-1-7281-3293-8. S2CID 54482423.
Karras, Tero; Laine, Samuli; Aittala, Miika; Hellsten, Janne; Lehtinen, Jaakko; Aila, Timo (June 2020). "Analyzing and Improving the Image Quality of StyleGAN". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. pp. 8107–8116. arXiv:1912.04958. doi:10.1109/cvpr42600.2020.00813. ISBN 978-1-7281-7168-5. S2CID 209202273.
Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan (February 1, 2017). "Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit". Monthly Notices of the Royal Astronomical Society: Letters. 467 (1): L110 – L114. arXiv:1702.00403. Bibcode:2017MNRAS.467L.110S. doi:10.1093/mnrasl/slx008. S2CID 7213940.
Mustafa, Mustafa; Bard, Deborah; Bhimji, Wahid; Lukić, Zarija; Al-Rfou, Rami; Kratochvil, Jan M. (May 6, 2019). "CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks". Computational Astrophysics and Cosmology. 6 (1): 1. arXiv:1706.02390. Bibcode:2019ComAC...6....1M. doi:10.1186/s40668-019-0029-9. ISSN 2197-7909. S2CID 126034204.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin (2017). "Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis". Computing and Software for Big Science. 1 (1): 4. arXiv:1701.05927. Bibcode:2017CSBS....1....4D. doi:10.1007/s41781-017-0004-6. S2CID 88514467.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin (2018). "Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters". Physical Review Letters. 120 (4): 042003. arXiv:1705.02355. Bibcode:2018PhRvL.120d2003P. doi:10.1103/PhysRevLett.120.042003. PMID 29437460. S2CID 3330974.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin (2018). "CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks". Phys. Rev. D. 97 (1): 014021. arXiv:1712.10321. Bibcode:2018PhRvD..97a4021P. doi:10.1103/PhysRevD.97.014021. S2CID 41265836.
Erdmann, Martin; Glombitza, Jonas; Quast, Thorben (2019). "Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network". Computing and Software for Big Science. 3 (1): 4. arXiv:1807.01954. Bibcode:2019CSBS....3....4E. doi:10.1007/s41781-018-0019-7. S2CID 54216502.
Musella, Pasquale; Pandolfi, Francesco (2018). "Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks". Computing and Software for Big Science. 2 (1): 8. arXiv:1805.00850. Bibcode:2018CSBS....2....8M. doi:10.1007/s41781-018-0015-y. S2CID 119474793.
SHiP, Collaboration (2019). "Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks". Journal of Instrumentation. 14 (11): 11028. arXiv:1909.04451. Bibcode:2019JInst..14P1028A. doi:10.1088/1748-0221/14/11/P11028. S2CID 202542604.
Nista, Ludovico; Pitsch, Heinz; Schumann, Christoph D. K.; Bode, Mathis; Grenga, Temistocle; MacArt, Jonathan F.; Attili, Antonio (June 4, 2024). "Influence of adversarial training on super-resolution turbulence reconstruction". Physical Review Fluids. 9 (6): 064601. arXiv:2308.16015. Bibcode:2024PhRvF...9f4601N. doi:10.1103/PhysRevFluids.9.064601.
Nista, L.; Schumann, C. D. K.; Grenga, T.; Attili, A.; Pitsch, H. (January 1, 2023). "Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows". Proceedings of the Combustion Institute. 39 (4): 5279–5288. Bibcode:2023PComI..39.5279N. doi:10.1016/j.proci.2022.07.244. ISSN 1540-7489.
Fukami, Kai; Fukagata, Koji; Taira, Kunihiko (August 1, 2020). "Assessment of supervised machine learning methods for fluid flows". Theoretical and Computational Fluid Dynamics. 34 (4): 497–519. arXiv:2001.09618. Bibcode:2020ThCFD..34..497F. doi:10.1007/s00162-020-00518-y. ISSN 1432-2250.
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