Manifold Learning by Mixture Models of VAEs for Inverse Problems

27 March 2023

Papers citing "Manifold Learning by Mixture Models of VAEs for Inverse Problems"

28 / 28 papers shown

Title
Your diffusion model secretly knows the dimension of the data manifold Jan Stanczuk Georgios Batzolis Teo Deveney Carola-Bibiane Schönlieb DiffM 67 29 0 23 Dec 2022
Proximal Residual Flows for Bayesian Inverse Problems J. Hertrich BDL TPM 62 4 0 30 Nov 2022
A Neural-Network-Based Convex Regularizer for Inverse Problems Alexis Goujon Sebastian Neumayer Pakshal Bohra Stanislas Ducotterd M. Unser 66 27 0 22 Nov 2022
Identifiability of deep generative models without auxiliary information Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam DRL 88 52 0 20 Jun 2022
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization Fabian Altekrüger Alexander Denker Paul Hagemann J. Hertrich Peter Maass Gabriele Steidl MedIm 50 26 0 24 May 2022
VQ-Flows: Vector Quantized Local Normalizing Flows Sahil Sidheekh Chris B. Dock Tushar Jain R. Balan M. Singh 56 8 0 22 Mar 2022
Generative Adversarial Networks Gilad Cohen Raja Giryes GAN 273 30,109 0 01 Mar 2022
Wasserstein Patch Prior for Image Superresolution J. Hertrich Antoine Houdard C. Redenbach SupR MDE 62 22 0 27 Sep 2021
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint Paul Hagemann J. Hertrich Gabriele Steidl BDL 68 39 0 23 Sep 2021
Data-driven discovery of intrinsic dynamics D. Floryan M. Graham AI4CE 131 75 0 12 Aug 2021
Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior Mario González Andrés Almansa Pauline Tan 51 31 0 02 Mar 2021
Trumpets: Injective Flows for Inference and Inverse Problems K. Kothari AmirEhsan Khorashadizadeh Maarten V. de Hoop Ivan Dokmanić TPM 41 50 0 20 Feb 2021
Deep Learning Techniques for Inverse Problems in Imaging Greg Ongie A. Jalal Christopher A. Metzler Richard G. Baraniuk A. Dimakis Rebecca Willett 81 533 0 12 May 2020
Learning Flat Latent Manifolds with VAEs Nutan Chen Alexej Klushyn Francesco Ferroni Justin Bayer Patrick van der Smagt DRL 56 41 0 12 Feb 2020
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 258 3,902 0 12 Jul 2019
Solving Electrical Impedance Tomography with Deep Learning Yuwei Fan Lexing Ying 72 103 0 06 Jun 2019
Diagnosing and Enhancing VAE Models Bin Dai David Wipf DRL 59 380 0 14 Mar 2019
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl Ricky T. Q. Chen J. Bettencourt Ilya Sutskever David Duvenaud DRL 139 873 0 02 Oct 2018
Analyzing Inverse Problems with Invertible Neural Networks Lynton Ardizzone Jakob Kruse Sebastian J. Wirkert D. Rahner E. Pellegrini R. Klessen Lena Maier-Hein Carsten Rother Ullrich Kothe 55 490 0 14 Aug 2018
Glow: Generative Flow with Invertible 1x1 Convolutions Diederik P. Kingma Prafulla Dhariwal BDL DRL 292 3,129 0 09 Jul 2018
InfoCatVAE: Representation Learning with Categorical Variational Autoencoders Edouard Pineau Marc Lelarge DRL 60 13 0 20 Jun 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 414 5,103 0 19 Jun 2018
Adversarial Regularizers in Inverse Problems Sebastian Lunz Ozan Oktem Carola-Bibiane Schönlieb GAN MedIm 84 220 0 29 May 2018
Hyperspherical Variational Auto-Encoders Tim R. Davidson Luca Falorsi Nicola De Cao Thomas Kipf Jakub M. Tomczak DRL BDL 104 385 0 03 Apr 2018
Neural Autoregressive Flows Chin-Wei Huang David M. Krueger Alexandre Lacoste Aaron Courville DRL AI4CE 141 442 0 03 Apr 2018
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks S. Hamilton A. Hauptmann 77 256 0 08 Nov 2017
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 263 3,696 0 26 May 2016
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 256 12,435 0 24 Jun 2012