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Lucas Theis | Casper Kaae Sønderby | Wenzhe Shi | Jose Caballero | Ferenc Huszár | Lucas Theis | C. Sønderby | Jose Caballero | Wenzhe Shi | Ferenc Huszár
[1] Béla Julesz,et al. Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.
[2] Zhou Wang,et al. Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
[3] Nikolay N. Ponomarenko,et al. TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .
[4] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[5] Harri Valpola,et al. Denoising Source Separation , 2005, J. Mach. Learn. Res..
[6] Michael Elad,et al. Example-based single document image super-resolution: a global MAP approach with outlier rejection , 2007, Multidimens. Syst. Signal Process..
[7] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[8] Valero Laparra,et al. Divisive normalization image quality metric revisited. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.
[9] Thomas S. Huang,et al. Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.
[10] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[11] M. Bethge,et al. Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations , 2011, PloS one.
[12] Thomas B. Moeslund,et al. Super-resolution: a comprehensive survey , 2014, Machine Vision and Applications.
[13] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[14] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[15] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[16] Leon A. Gatys,et al. Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.
[17] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[18] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[19] Colin Raffel,et al. Lasagne: First release. , 2015 .
[20] Richard G. Baraniuk,et al. Optimal recovery from compressive measurements via denoising-based approximate message passing , 2015, 2015 International Conference on Sampling Theory and Applications (SampTA).
[21] Tapani Raiko,et al. Semi-supervised Learning with Ladder Networks , 2015, NIPS.
[22] Matthias Bethge,et al. Generative Image Modeling Using Spatial LSTMs , 2015, NIPS.
[23] Daniel Rueckert,et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Shakir Mohamed,et al. Learning in Implicit Generative Models , 2016, ArXiv.
[25] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[26] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[27] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[28] Xiaoou Tang,et al. Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Harri Valpola,et al. Tagger: Deep Unsupervised Perceptual Grouping , 2016, NIPS.
[30] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[31] Chuan Li,et al. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[33] Thomas Brox,et al. Generating Images with Perceptual Similarity Metrics based on Deep Networks , 2016, NIPS.
[34] Joan Bruna,et al. Super-Resolution with Deep Convolutional Sufficient Statistics , 2015, ICLR.
[35] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[36] Valero Laparra,et al. Perceptual image quality assessment using a normalized Laplacian pyramid , 2016, HVEI.
[37] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[38] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[40] 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).