Can Push-forward Generative Models Fit Multimodal Distributions?

Can Push-forward Generative Models Fit Multimodal Distributions?

29 June 2022

Antoine Salmona

Valentin De Bortoli

Papers citing "Can Push-forward Generative Models Fit Multimodal Distributions?"

11 / 11 papers shown

Title
Particle Semi-Implicit Variational Inference Jen Ning Lim A. M. Johansen 41 3 0 30 Jun 2024
Stable Training of Normalizing Flows for High-dimensional Variational Inference Daniel Andrade BDL TPM 37 1 0 26 Feb 2024
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction Moritz Piening Fabian Altekrüger J. Hertrich Paul Hagemann Andrea Walther Gabriele Steidl 24 6 0 27 Dec 2023
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates Stefano Bruno Ying Zhang Dong-Young Lim Ömer Deniz Akyildiz Sotirios Sabanis DiffM 27 4 0 22 Nov 2023
Closed-Form Diffusion Models Christopher Scarvelis Haitz Sáez de Ocáriz Borde Justin Solomon DiffM 95 9 0 19 Oct 2023
Balanced Training of Energy-Based Models with Adaptive Flow Sampling Louis Grenioux Eric Moulines Marylou Gabrié 13 2 0 01 Jun 2023
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems Fabian Altekrüger Paul Hagemann Gabriele Steidl TPM 21 9 0 28 Mar 2023
Proximal Residual Flows for Bayesian Inverse Problems J. Hertrich BDL TPM 25 4 0 30 Nov 2022
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 189 260 0 18 Apr 2021
Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros SSeg 212 19,444 0 21 Nov 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,190 0 01 Sep 2014