暂无分享,去创建一个
Jaakko Lehtinen | Timo Aila | Samuli Laine | Tero Karras | Tero Karras | Timo Aila | S. Laine | J. Lehtinen
[1] Oscar Firschein,et al. Readings in computer vision: issues, problems, principles, and paradigms , 1987 .
[2] Bernd Fritzke,et al. A Growing Neural Gas Network Learns Topologies , 1994, NIPS.
[3] Risto Miikkulainen,et al. Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.
[4] Zhou Wang,et al. Multi-scale structural similarity for image quality assessment , 2003 .
[5] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[6] Julien Rabin,et al. Wasserstein Barycenter and Its Application to Texture Mixing , 2011, SSVM.
[7] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[8] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[9] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[10] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[11] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[12] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[16] Yu-Bin Yang,et al. Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections , 2016, ArXiv.
[17] Tim Salimans,et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.
[18] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[19] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[20] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[21] Geoffrey E. Hinton,et al. Layer Normalization , 2016, ArXiv.
[22] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[23] Philip Bachman,et al. Calibrating Energy-based Generative Adversarial Networks , 2017, ICLR.
[24] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[25] Vladlen Koltun,et al. Photographic Image Synthesis with Cascaded Refinement Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Jacob D. Abernethy,et al. How to Train Your DRAGAN , 2017, ArXiv.
[27] Pablo M. Granitto,et al. Class-Splitting Generative Adversarial Networks , 2017, ArXiv.
[28] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[29] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[30] Marco Marchesi,et al. Megapixel Size Image Creation using Generative Adversarial Networks , 2017, ArXiv.
[31] Yoshua Bengio,et al. Improving Generative Adversarial Networks with Denoising Feature Matching , 2016, ICLR.
[32] Twan van Laarhoven,et al. L2 Regularization versus Batch and Weight Normalization , 2017, ArXiv.
[33] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[35] John E. Hopcroft,et al. Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] David Berthelot,et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.
[37] Dhruv Batra,et al. LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation , 2016, ICLR.
[38] Hiroshi Ishikawa,et al. Globally and locally consistent image completion , 2017, ACM Trans. Graph..
[39] 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).
[40] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[41] 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).
[42] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[43] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[44] Yi Zhang,et al. Do GANs actually learn the distribution? An empirical study , 2017, ArXiv.
[45] Aaron C. Courville,et al. Adversarially Learned Inference , 2016, ICLR.
[46] Philip H. S. Torr,et al. Multi-agent Diverse Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[47] Jan Kautz,et al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[48] Ashish Khetan,et al. PacGAN: The Power of Two Samples in Generative Adversarial Networks , 2017, IEEE Journal on Selected Areas in Information Theory.