StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
暂无分享,去创建一个
Dimitris N. Metaxas | Tao Xu | Han Zhang | Hongsheng Li | Han Zhang | Xiaogang Wang | Xiaolei Huang | Shaoting Zhang | Han Zhang | Tao Xu | Hongsheng Li
[1] Andrew Zisserman,et al. Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.
[2] Pietro Perona,et al. The Caltech-UCSD Birds-200-2011 Dataset , 2011 .
[3] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[4] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[5] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[6] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[7] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[8] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[9] Scott E. Reed,et al. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis , 2015, NIPS.
[10] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[11] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[12] Jon Gauthier. Conditional generative adversarial nets for convolutional face generation , 2015 .
[13] Yuting Zhang,et al. Deep Visual Analogy-Making , 2015, NIPS.
[14] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[15] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[16] Thomas Brox,et al. Learning to generate chairs with convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Ruslan Salakhutdinov,et al. Generating Images from Captions with Attention , 2015, ICLR.
[18] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[19] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Carl Doersch,et al. Tutorial on Variational Autoencoders , 2016, ArXiv.
[21] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[22] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Honglak Lee,et al. Attribute2Image: Conditional Image Generation from Visual Attributes , 2015, ECCV.
[24] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[25] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[26] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[27] Bernt Schiele,et al. Generative Adversarial Text to Image Synthesis , 2016, ICML.
[28] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[29] Bernt Schiele,et al. Learning Deep Representations of Fine-Grained Visual Descriptions , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Abhinav Gupta,et al. Generative Image Modeling Using Style and Structure Adversarial Networks , 2016, ECCV.
[31] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[32] Bernt Schiele,et al. Learning What and Where to Draw , 2016, NIPS.
[33] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[34] Alexei A. Efros,et al. Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.
[35] Lior Wolf,et al. Unsupervised Cross-Domain Image Generation , 2016, ICLR.
[36] Yoshua Bengio,et al. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Andrew Brock,et al. Neural Photo Editing with Introspective Adversarial Networks , 2016, ICLR.
[38] Lucas Theis,et al. Amortised MAP Inference for Image Super-resolution , 2016, ICLR.
[39] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[40] Yann LeCun,et al. Energy-based Generative Adversarial Networks , 2016, ICLR.
[41] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[43] John E. Hopcroft,et al. Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Nando de Freitas,et al. Generating Interpretable Images with Controllable Structure , 2017 .
[45] 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).
[46] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[47] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.