GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)

[1]  Jiagen Hou,et al.  Geological Facies modeling based on progressive growing of generative adversarial networks (GANs) , 2020, Computational Geosciences.

[2]  J. Caers,et al.  A Tree‐Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling , 2020, Water Resources Research.

[3]  T. Mukerji,et al.  Geomodeling Using Generative Adversarial Networks and a Database of Satellite Imagery of Modern River Deltas , 2019 .

[4]  Zhi Zhong,et al.  Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network , 2019, Water Resources Research.

[5]  Lin Liang,et al.  Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks , 2019, Petroleum Science.

[6]  J. Leinonen,et al.  Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network , 2019, Geophysical Research Letters.

[7]  Timo Aila,et al.  A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Ahmed H. Elsheikh,et al.  Parametric generation of conditional geological realizations using generative neural networks , 2018, Computational Geosciences.

[9]  Martin J. Blunt,et al.  Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks as a Geological Prior , 2018, Mathematical Geosciences.

[10]  Ali Borji,et al.  Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..

[11]  Alexander Y. Sun,et al.  Discovering State‐Parameter Mappings in Subsurface Models Using Generative Adversarial Networks , 2018, Geophysical Research Letters.

[12]  Lukas Mosser,et al.  Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks , 2018, 80th EAGE Conference and Exhibition 2018.

[13]  Gözde B. Ünal,et al.  Patch-Based Image Inpainting with Generative Adversarial Networks , 2018, ArXiv.

[14]  Emilien Dupont,et al.  Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks , 2018, 1802.03065.

[15]  Martin J. Blunt,et al.  Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks , 2017, Transport in Porous Media.

[16]  Mario Lucic,et al.  Are GANs Created Equal? A Large-Scale Study , 2017, NeurIPS.

[17]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[18]  Harshad Rai,et al.  Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks , 2018 .

[19]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[20]  Eric Laloy,et al.  Training‐Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network , 2017, ArXiv.

[21]  Ahmed H. Elsheikh,et al.  Parametrization and generation of geological models with generative adversarial networks , 2017, 1708.01810.

[22]  Luc Van Gool,et al.  Pose Guided Person Image Generation , 2017, NIPS.

[23]  Martin J. Blunt,et al.  Reconstruction of three-dimensional porous media using generative adversarial neural networks , 2017, Physical review. E.

[24]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[25]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[26]  Dimitris N. Metaxas,et al.  StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[27]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Aaron C. Courville,et al.  Adversarially Learned Inference , 2016, ICLR.

[30]  Olof Mogren,et al.  C-RNN-GAN: Continuous recurrent neural networks with adversarial training , 2016, ArXiv.

[31]  Jiajun Wu,et al.  Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.

[32]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[33]  Alex Graves,et al.  Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.

[34]  Bernt Schiele,et al.  Generative Adversarial Text to Image Synthesis , 2016, ICML.

[35]  Aldo Clerici,et al.  A Set of GRASS GIS-Based Shell Scripts for the Calculation and Graphical Display of the Main Morphometric Parameters of a River Channel , 2016 .

[36]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[37]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[38]  Zhe Gan,et al.  Learning Deep Sigmoid Belief Networks with Data Augmentation , 2015, AISTATS.

[39]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[40]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[41]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[43]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[44]  Hugo Larochelle,et al.  The Neural Autoregressive Distribution Estimator , 2011, AISTATS.