Fetching the paper…
Reading the bibliography…
Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity.
Generative Adversarial Nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS’14) . MIT Press, Cambridge, MA, USA, 2672–2680
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. 2015 · 2015
Earlier work this paper cites.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 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 · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2818–2826
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017 · 2017
Earlier work this paper cites.
DeLiGAN: Generative adversarial networks for diverse and limited data. In Proc. CVPR . 166–174
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R. Venkatesh Babu. 2017 · 2017
Earlier work this paper cites.
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
Megapixel size image creation using generative adversarial networks
Marco Marchesi. 2017 · 2017
Earlier work this paper cites.
On gradient regularizers for MMD GANs
Michael Arbel, Dougal J Sutherland, Mikołaj Bińkowski, and Arthur Gretton. 2018 · 2018
Cited alongside, same era.
Matan Ben-Yosef and Daphna Weinshall. 2018 · 2018
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018 · 2018
Cited alongside, same era.
Multi-agent diverse generative adversarial networks. In Proc. CVPR . 8513–8521
Arnab Ghosh, Viveka Kulharia, Vinay P Namboodiri, Philip HS Torr, and Puneet K Dokania. 2018 · 2018
Cited alongside, same era.
MGAN: Training generative adversarial nets with multiple generators. In Proc. ICLR
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung. 2018 · 2018
A style-based generator architecture for generative adversarial networks. In Proc. CVPR . 4401–4410
Tero Karras, Samuli Laine, and Timo Aila. 2019 · 2019
Later among the works it cites.
Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2019 · 2019
Later among the works it cites.
ClusterGAN: Latent space clustering in generative adversarial networks
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, and Sreeram Kannan. 2019 · 2019
Later among the works it cites.
Mmgan: Generative adversarial networks for multi-modal distributions
Teodora Pandeva and Matthias Schubert. 2019 · 2019
Later among the works it cites.
Analyzing and improving the image quality of stylegan. In Proc. CVPR . 8110–8119
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Disconnected manifold learning for generative adversarial networks
Mahyar Khayatkhoei, Ahmed Elgammal, and Maneesh Singh. 2018 · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal. 2018 · 2018
Cited alongside, same era.
Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly. 2018 · 2018
Cited alongside, same era.
Image2stylegan: How to embed images into the stylegan latent space?. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 4432–4441
Rameen Abdal, Yipeng Qin, and Peter Wonka. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training
Tin Kramberger and Božidar Potočnik. 2020 · 2020
Later among the works it cites.
Diverse image generation via self-conditioned GANs. In Proc. CVPR . 14286–14295
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, and Antonio Torralba. 2020 · 2020
Later among the works it cites.
Unsupervised K-Modal Styled Content Generation
Omry Sendik, Dani Lischinski, and Daniel Cohen-Or. 2020 · 2020
Later among the works it cites.
Learning disconnected manifolds: a no GAN’s land. In Proc. ICML . PMLR, 9418–9427
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, and Jérémie Mary. 2020 · 2020
Later among the works it cites.