Metrics for Deep Generative Models

3 November 2017

Patrick van der Smagt

SyDa

DRL

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Papers citing "Metrics for Deep Generative Models"

24 / 24 papers shown

Title
Image Interpolation with Score-based Riemannian Metrics of Diffusion Models Shinnosuke Saito Takashi Matsubara DiffM 82 1 0 28 Apr 2025
SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation Mingze Sun Chen Guo Puhua Jiang Shiwei Mao Yurun Chen Ruqi Huang 69 4 0 18 Sep 2024
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation Melanie F. Pradier Javier González CML 50 0 0 09 Sep 2024
Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency Junhao Chen Manyi Li Zherong Pan Xifeng Gao Changhe Tu DiffM 43 2 0 07 Jun 2024
Pulling back symmetric Riemannian geometry for data analysis W. Diepeveen 32 2 0 11 Mar 2024
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry Yong-Hyun Park Mingi Kwon J. Choi Junghyo Jo Youngjung Uh DiffM 40 60 0 24 Jul 2023
Variational Inference for Longitudinal Data Using Normalizing Flows Clément Chadebec S. Allassonnière BDL DRL 26 1 0 24 Mar 2023
Unsupervised Discovery of Semantic Latent Directions in Diffusion Models Yong-Hyun Park Mingi Kwon Junghyo Jo Youngjung Uh DiffM 41 22 0 24 Feb 2023
Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model Takehiro Aoshima Takashi Matsubara 47 4 0 26 Nov 2022
A Geometric Perspective on Variational Autoencoders Clément Chadebec S. Allassonnière DRL 37 21 0 15 Sep 2022
Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features Guha Balakrishnan Raghudeep Gadde Aleix M. Martinez Pietro Perona 44 3 0 25 Jan 2022
Exploring the Latent Space of Autoencoders with Interventional Assays Felix Leeb Stefan Bauer M. Besserve Bernhard Schölkopf DRL 49 17 0 30 Jun 2021
Pulling back information geometry Georgios Arvanitidis Miguel González Duque Alison Pouplin Dimitris Kalatzis Søren Hauberg DRL 30 14 0 09 Jun 2021
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder Clément Chadebec Elina Thibeau-Sutre Ninon Burgos S. Allassonnière 45 62 0 30 Apr 2021
Geometry-Aware Hamiltonian Variational Auto-Encoder Clément Chadebec Clément Mantoux S. Allassonnière DRL 24 15 0 22 Oct 2020
Geometrically Enriched Latent Spaces Georgios Arvanitidis Søren Hauberg Bernhard Schölkopf DRL 19 51 0 02 Aug 2020
Variational Autoencoder with Learned Latent Structure Marissa Connor Gregory H. Canal Christopher Rozell CML DRL 29 42 0 18 Jun 2020
Learning Flat Latent Manifolds with VAEs Nutan Chen Alexej Klushyn Francesco Ferroni Justin Bayer Patrick van der Smagt DRL 35 39 0 12 Feb 2020
Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology Jiseob Kim Byoung-Tak Zhang 6 2 0 20 Jan 2019
Fast Approximate Geodesics for Deep Generative Models Nutan Chen Francesco Ferroni Alexej Klushyn A. Paraschos Justin Bayer Patrick van der Smagt DRL 19 30 0 19 Dec 2018
Active Learning based on Data Uncertainty and Model Sensitivity Nutan Chen Alexej Klushyn A. Paraschos Djalel Benbouzid Patrick van der Smagt 13 17 0 06 Aug 2018
Only Bayes should learn a manifold (on the estimation of differential geometric structure from data) Søren Hauberg 19 31 0 13 Jun 2018
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds Daniele Grattarola Daniele Zambon Cesare Alippi L. Livi GNN 35 40 0 16 May 2018
Is Generator Conditioning Causally Related to GAN Performance? Augustus Odena Jacob Buckman Catherine Olsson Tom B. Brown C. Olah Colin Raffel Ian Goodfellow AI4CE 35 112 0 23 Feb 2018