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Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions.
Some recent researches on the motion of fluids
Harry Bateman · 1915
Earlier work this paper cites.
Free energy of a nonuniform system. i. interfacial free energy
John W Cahn and John E Hilliard · 1958
Earlier work this paper cites.
A 3x3 isotropic gradient operator for image processing, 1968
Irwin Sobel and Gary Feldman · 1968
Earlier work this paper cites.
Statistical reconstruction of three-dimensional porous media from two-dimensional images
Anthony P Roberts · 1997
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Reconstructing random media
C. L. Y. Yeong and S. Torquato · 1998
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Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method
Baskar Ganapathysubramanian and Nicholas Zabaras · 2007
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A non-linear dimension reduction methodology for generating data-driven stochastic input models
Baskar Ganapathysubramanian and Nicholas Zabaras · 2008
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Modeling morphology evolution during solvent-based fabrication of organic solar cells
Olga Wodo and Baskar Ganapathysubramanian · 2012
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Multiple-point geostatistical modeling based on the cross-correlation functions
Pejman Tahmasebi, Ardeshir Hezarkhani, and Muhammad Sahimi · 2012
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Random heterogeneous materials: microstructure and macroscopic properties
Salvatore Torquato · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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
Earlier work this paper cites.
Towards the automatic anime characters creation with generative adversarial networks
Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, and Zhihao Fang · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networkss
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
Cited alongside, same era.
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Deep learning the physics of transport phenomena
Amir Barati Farimani, Joseph Gomes, and Vijay S. Pande · 2017
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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Yinhao Zhu and Nicholas Zabaras · 2018
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Enforcing constraints for interpolation and extrapolation in generative adversarial networks
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, and Enoch Yeung · 2018
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Constrained image generation using binarized neural networks with decision procedures
Svyatoslav Korneev, Nina Narodytska, Luca Pulina, Armando Tacchella, Nikolaj Bjorner, and Mooly Sagiv · 2018
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Do gan loss functions really matter?
Yipeng Qin, Niloy Jyoti Mitra, and Peter Wonka · 2018
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On the limitations of first-order approximation in gan dynamics
Jerry Li, Aleksander Madry, John Peebles, and Ludwig Schmidt · 2018
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Learning particle physics by example: location-aware generative adversarial networks for physics synthesis
Luke de Oliveira, Michela Paganini, and Benjamin Nachman · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J. Zico Kolter · 2017
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Training gans with optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
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Amortised map inference for image super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2017
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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
Cited alongside, same era.
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Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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On convergence and stability of gans
Naveen Kodali, Jacob D. Abernethy, James Hays, and Zsolt Kira · 2018
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Accelerating multi-point statistics reconstruction method for porous media via deep learning
Junxi Feng, Qizhi Teng, Xiaohai He, and Xiaohong Wu · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, and Paris Perdikaris · 2019
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Spatially constrained generative adversarial networks for conditional image generation
Songyao Jiang, Hongfu Liu, Yue Wu, and Yun Fu · 2019
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The unusual effectiveness of averaging in gan training
Yasin Yazici, Chuan Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, and Vijay Chandrasekhar · 2019
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Interaction matters: A note on non-asymptotic local convergence of generative adversarial networks
Tengyuan Liang and James Stokes · 2019
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Aryan Mokhtari, Asuman Ozdaglar, and Sarath Pattathil · 2019
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Binary 2d morphologies of polymer phase separation
Balaji Sesha Sarath Pokuri, Viraj Shah, Ameya Joshi, Chinmay Hegde, Soumik Sarkar, and Baskar Ganapathysubramanian · 2019
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