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

Authors

150

  1. SK
    Sreeram Kannan

    University of Washington, United States

    2 publications · 380 citations

    2

    h-index

  2. AJ
    Alec Jacobson

    University of Toronto, Canada

    2 publications · 60 citations

    2

    h-index

  3. LV
    Luc Van Gool

    ETH Zurich, Switzerland

    2 publications · 20 citations

    2

    h-index

  4. KL
    Kyogu Lee

    Seoul National University, South Korea

    2 publications · 37 citations

    2

    h-index

  5. EL
    Eugene Lin

    China Medical University, Taiwan

    2 publications · 380 citations

    2

    h-index

1–5 of 150

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 542 citations

    10

    h-index

  2. ACM Transactions on Graphics

    TOG · 1 publications · 20 citations

    1

    h-index

  3. RECERCAT (Consorci de Serveis Universitaris de Catalunya)

    Repository · 1 publications · 30 citations

    1

    h-index

  4. PLoS Computational Biology

    1 publications · 55 citations

    1

    h-index

  5. Computer Graphics Forum

    1 publications · 246 citations

    1

    h-index

1–5