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Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 1912
Earlier work this paper cites.
Probabilistic principal component analysis
Michael E Tipping and Christopher M Bishop · 1999
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Feature quantization improves gan training
Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, and Changyou Chen · 2004
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A review of instance selection methods
J Arturo Olvera-López, J Ariel Carrasco-Ochoa, J Francisco Martínez-Trinidad, and Josef Kittler · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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A density-based approach for instance selection
Joel Luis Carbonera and Mara Abel · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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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
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Megapixel size image creation using generative adversarial networks
Marco Marchesi · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 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.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
Cited alongside, same era.
Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
Later among the works it cites.
Classification accuracy score for conditional generative models
Suman Ravuri and Oriol Vinyals · 2019
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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.
Logan: Latent optimisation for generative adversarial networks
Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, and Timothy P. Lillicrap · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Cited alongside, same era.
Training dataset reduction on generative adversarial network
Fajar Ulin Nuha et al · 2018
Cited alongside, same era.
How good is my gan?
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2018
Cited alongside, same era.
Metropolis-hastings generative adversarial networks
Ryan C Turner, Jane Hung, Yunus Saatci, and Jason Yosinski · 2018
Cited alongside, same era.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Cited alongside, same era.
Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
Cited alongside, same era.
Discriminator rejection sampling
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, and Augustus Odena · 2019
Cited alongside, same era.
Closest in time.
Your gan is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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Subsampling generative adversarial networks: Density ratio estimation in feature space with softplus loss
Xin Ding, Z. Jane Wang, and William J. Welch · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Top-k training of gans: Improving generators by making critics less critical
Samarth Sinha, Anirudh Goyal, Colin Raffel, and Augustus Odena · 2020
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Deepfakes and beyond: A survey of face manipulation and fake detection
Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia · 2020
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In-domain gan inversion for real image editing
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou · 2020
Closest in time.