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Analyzing Generative Models by Manifold Entropic Metrics

25 October 2024

Papers citing "Analyzing Generative Models by Manifold Entropic Metrics"

35 / 35 papers shown

Title
Robustness of Nonlinear Representation Learning Simon Buchholz Bernhard Schölkopf OOD 412 4 0 19 Mar 2025
The Road Less Scheduled Aaron Defazio Xingyu Yang Yang Harsh Mehta Konstantin Mishchenko Ahmed Khaled Ashok Cutkosky 96 59 0 24 May 2024
Free-form Flows: Make Any Architecture a Normalizing Flow Felix Dräxler Peter Sorrenson Lea Zimmermann Armand Rousselot Ullrich Kothe TPM DRL AI4CE BDL 83 11 0 25 Oct 2023
A Review of Change of Variable Formulas for Generative Modeling Ullrich Kothe 56 8 0 04 Aug 2023
Compositional Generalization from First Principles Thaddäus Wiedemer Prasanna Mayilvahanan Matthias Bethge Wieland Brendel OCL 81 46 0 10 Jul 2023
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning Aapo Hyvarinen Ilyes Khemakhem H. Morioka CML OOD 91 37 0 29 Mar 2023
Identifiability of latent-variable and structural-equation models: from linear to nonlinear Aapo Hyvarinen Ilyes Khemakhem R. Monti CML 82 45 0 06 Feb 2023
Disentangled Representation Learning Xin Eric Wang Hong Chen Siao Tang Zihao Wu Wenwu Zhu DRL 101 87 0 21 Nov 2022
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case Clément Chadebec Louis J. Vincent S. Allassonnière DRL 73 30 0 16 Jun 2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis Patrik Reizinger Luigi Gresele Jack Brady Julius von Kügelgen Dominik Zietlow Bernhard Schölkopf Georg Martius Wieland Brendel M. Besserve DRL 68 20 0 06 Jun 2022
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows Eike Cramer Felix Rauh Alexander Mitsos Raúl Tempone Manuel Dahmen DRL 59 9 0 08 Mar 2022
On Causally Disentangled Representations Abbavaram Gowtham Reddy Benin Godfrey L V. Balasubramanian OOD CML 90 22 0 10 Dec 2021
Disentanglement Analysis with Partial Information Decomposition Seiya Tokui Issei Sato CoGe DRL 71 15 0 31 Aug 2021
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows Brendan Leigh Ross Jesse C. Cresswell TPM 73 32 0 09 Jun 2021
Independent mechanism analysis, a new concept? Luigi Gresele Julius von Kügelgen Vincent Stimper Bernhard Schölkopf M. Besserve CML 68 102 0 09 Jun 2021
Rectangular Flows for Manifold Learning Anthony L. Caterini Gabriel Loaiza-Ganem Geoff Pleiss John P. Cunningham DRL 66 47 0 02 Jun 2021
Trumpets: Injective Flows for Inference and Inverse Problems K. Kothari AmirEhsan Khorashadizadeh Maarten V. de Hoop Ivan Dokmanić TPM 48 50 0 20 Feb 2021
Wavelet Flow: Fast Training of High Resolution Normalizing Flows Jason J. Yu Konstantinos G. Derpanis Marcus A. Brubaker TPM 150 41 0 26 Oct 2020
Flows for simultaneous manifold learning and density estimation Johann Brehmer Kyle Cranmer DRL AI4CE 90 163 0 31 Mar 2020
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space A. Voynov Artem Babenko 141 419 0 10 Feb 2020
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN) Peter Sorrenson Carsten Rother Ullrich Kothe DRL CML 65 121 0 14 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 532 42,591 0 03 Dec 2019
Theory and Evaluation Metrics for Learning Disentangled Representations Kien Do T. Tran CoGe DRL 74 96 0 26 Aug 2019
Guided Image Generation with Conditional Invertible Neural Networks Lynton Ardizzone Carsten T. Lüth Jakob Kruse Carsten Rother Ullrich Kothe DRL 86 294 0 04 Jul 2019
Neural Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios DRL 186 777 0 10 Jun 2019
Unsupervised Model Selection for Variational Disentangled Representation Learning Sunny Duan Loic Matthey Andre Saraiva Nicholas Watters Christopher P. Burgess Alexander Lerchner I. Higgins OOD DRL 71 80 0 29 May 2019
Relevance Factor VAE: Learning and Identifying Disentangled Factors Minyoung Kim Yuting Wang Pritish Sahu Vladimir Pavlovic CoGe CML DRL 69 40 0 05 Feb 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 124 1,471 0 29 Nov 2018
Glow: Generative Flow with Invertible 1x1 Convolutions Diederik P. Kingma Prafulla Dhariwal BDL DRL 300 3,141 0 09 Jul 2018
Learning Deep Disentangled Embeddings with the F-Statistic Loss Karl Ridgeway Michael C. Mozer FedML DRL CoGe 72 218 0 14 Feb 2018
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations Abhishek Kumar P. Sattigeri Avinash Balakrishnan BDL DRL 83 523 0 02 Nov 2017
EMNIST: an extension of MNIST to handwritten letters Gregory Cohen Saeed Afshar J. Tapson André van Schaik 65 720 0 17 Feb 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 275 3,716 0 26 May 2016
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions Insu Han Dmitry Malioutov Jinwoo Shin 58 95 0 22 Mar 2015
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 274 12,458 0 24 Jun 2012