Latent Space Interpolation
Latent Space Interpolation is a generative method that enables the continuous exploration of learned data manifolds by traversing paths between encoded representations within the latent space of a generative model.
37
Publications
2.6K
Citations
150
Authors
65
Institutions
Publications per year
2017–2026
35
Parents
37
Scaling up GANs for Text-to-Image Synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang et al. · 2023 · 368 citations
Computing, Design, Computational Art +13
Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals et al. · 2016 · 346 citations · Full text
ClusterGAN: Latent Space Clustering in Generative Adversarial Networks
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin et al. · 2019 · 321 citations · Full text
Improved Precision and Recall Metric for Assessing Generative Models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine et al. · arXiv (Cornell University) · 2019 · 250 citations · Full text
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65
University of Washington
Seattle, United States
3 h-index
9 publications
1.5K citations
University of California San Diego
San Diego, United States
2 h-index
7 publications
371 citations

McGill University
Montreal, Canada
2 h-index
2 publications
60 citations
Technical University of Munich
Munich, Germany
2 h-index
3 publications
296 citations
Mila - Quebec Artificial Intelligence Institute
Montreal, Canada
2 h-index
4 publications
137 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
408 citations
Tencent (China)
Shenzhen, China
2 h-index
4 publications
177 citations
ETH Zurich
Zurich, Switzerland
2 h-index
8 publications
1K citations
1–8 of 65
Repository · 11 publications · 542 citations
10
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TOG · 1 publications · 20 citations
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Repository · 1 publications · 30 citations
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1 publications · 55 citations
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1 publications · 246 citations
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