Fetching the paper…
Reading the bibliography…
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.
Time: Text and image mutual-translation adversarial networks
Bingchen Liu, Kunpeng Song, Yizhe Zhu, Gerard de Melo, and Ahmed Elgammal · 2005
Earlier work this paper cites.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2006
Earlier work this paper cites.
A visual vocabulary for flower classification
Maria-Elena Nilsback and Andrew Zisserman · 2006
Earlier work this paper cites.
Learning hybrid image templates (hit) by information projection
Zhangzhang Si and Song-Chun Zhu · 2011
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
Earlier work this paper cites.
Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
Earlier work this paper cites.
Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
Earlier work this paper cites.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Leon Bottou · 2017
Earlier work this paper cites.
Wasserstein gan, 2017
Martin Arjovsky, Soumith Chintala, and Leon Bottou · 2017
Earlier work this paper cites.
Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
Earlier work this paper cites.
Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
Earlier work this paper cites.
Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
Cited alongside, same era.
Stacked generative adversarial networks
Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Jae Hyun Lim and Jong Chul Ye · 2017
Cited alongside, same era.
Precise recovery of latent vectors from generative adversarial networks
Zachary C Lipton and Subarna Tripathi · 2017
Cited alongside, same era.
Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
Later among the works it cites.
Auto-embedding generative adversarial networks for high resolution image synthesis
Yong Guo, Qi Chen, Jian Chen, Qingyao Wu, Qinfeng Shi, and Mingkui Tan · 2019
Later among the works it cites.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Later among the works it cites.
Msg-gan: multi-scale gradient gan for stable image synthesis
Animesh Karnewar and Oliver Wang · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Deep and hierarchical implicit models
Dustin Tran, Rajesh Ranganath, and David M Blei · 2017
Cited alongside, same era.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Depthwisegans: Fast training generative adversarial networks for realistic image synthesis
Mkhuseli Ngxande, Jules-Raymond Tapamo, and Michael Burke · 2019
Later among the works it cites.
Image generation from small datasets via batch statistics adaptation
Atsuhiro Noguchi and Tatsuya Harada · 2019
Later among the works it cites.
Small-gan: Speeding up gan training using core-sets
Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, and Augustus Odena · 2019
Later among the works it cites.
Self-supervised gan: Analysis and improvement with multi-class minimax game
Ngoc-Trung Tran, Viet-Hung Tran, Bao-Ngoc Nguyen, Linxiao Yang, and Ngai-Man Man Cheung · 2019
Later among the works it cites.
Artists, artificial intelligence and machine-based creativity in playform
Ahmed Elgammal, Marian Mazzone, et al · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2020
Later among the works it cites.
Freeze discriminator: A simple baseline for fine-tuning gans
Sangwoo Mo, Minsu Cho, and Jinwoo Shin · 2020
Later among the works it cites.
Few-shot adaptation of generative adversarial networks
Esther Robb, Wen-Sheng Chu, Abhishek Kumar, and Jia-Bin Huang · 2020
Later among the works it cites.
Minegan: effective knowledge transfer from gans to target domains with few images
Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, and Joost van de Weijer · 2020
Later among the works it cites.
Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
Later among the works it cites.
Improving the speed and quality of gan by adversarial training
Jiachen Zhong, Xuanqing Liu, and Cho-Jui Hsieh · 2020
Later among the works it cites.
In-domain gan inversion for real image editing
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou · 2020
Later among the works it cites.