Unsupervised Model Selection for Variational Disentangled Representation Learning

29 May 2019

Christopher P. Burgess

Papers citing "Unsupervised Model Selection for Variational Disentangled Representation Learning"

23 / 23 papers shown

Title
Representational Similarity via Interpretable Visual Concepts Neehar Kondapaneni Oisin Mac Aodha Pietro Perona DRL 240 0 0 19 Mar 2025
Analyzing Generative Models by Manifold Entropic Metrics Daniel Galperin Ullrich Köthe DRL 30 0 0 25 Oct 2024
Predictive variational autoencoder for learning robust representations of time-series data Julia Huiming Wang Dexter Tsin Tatiana Engel CML OOD AI4TS 34 2 0 12 Dec 2023
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems David A. Klindt Sophia Sanborn Francisco Acosta Frédéric Poitevin Nina Miolane MILM FAtt 44 7 0 17 Oct 2023
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models Preben Ness D. Marijan Sunanda Bose CML 31 0 0 21 Aug 2023
A Category-theoretical Meta-analysis of Definitions of Disentanglement Yivan Zhang Masashi Sugiyama 38 3 0 11 May 2023
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling Romain Lopez Natavsa Tagasovska Stephen Ra K. Cho J. Pritchard Aviv Regev OOD CML DRL 36 35 0 07 Nov 2022
Graph Anomaly Detection with Unsupervised GNNs Lingxiao Zhao Saurabh Sawlani Arvind Srinivasan Leman Akoglu 39 17 0 18 Oct 2022
How do Variational Autoencoders Learn? Insights from Representational Similarity Lisa Bonheme M. Grzes CoGe SSL DRL 37 10 0 17 May 2022
Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation M. Montero J. Bowers Rui Ponte Costa Casimir J. H. Ludwig Gaurav Malhotra DRL CoGe 32 11 0 05 Apr 2022
Efficient-VDVAE: Less is more Louay Hazami Rayhane Mama Ragavan Thurairatnam BDL 29 28 0 25 Mar 2022
Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models Alon Jacovi Junyoung Lee Alexander Lerch DRL 23 1 0 20 Dec 2021
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning Thilini Cooray Ngai-man Cheung 27 6 0 16 Dec 2021
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Simone Foti Bongjin Koo Danail Stoyanov Matthew J. Clarkson CoGe 19 15 0 24 Nov 2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 50 8 0 10 Nov 2021
Where and What? Examining Interpretable Disentangled Representations Xinqi Zhu Chang Xu Dacheng Tao FAtt DRL 58 38 0 07 Apr 2021
Measuring Disentanglement: A Review of Metrics M. Carbonneau Julian Zaïdi Jonathan Boilard G. Gagnon CoGe DRL 28 81 0 16 Dec 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 14 66 0 27 Oct 2020
Robust Disentanglement of a Few Factors at a Time Benjamin Estermann Markus Marks M. Yanik CoGe OOD DRL 13 3 0 26 Oct 2020
A Commentary on the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD DRL 29 20 0 28 Jul 2020
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding David A. Klindt Lukas Schott Yash Sharma Ivan Ustyuzhaninov Wieland Brendel Matthias Bethge Dylan M. Paiton CML 48 132 0 21 Jul 2020
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks Yaniv Yacoby Weiwei Pan Finale Doshi-Velez CML DRL 29 25 0 14 Jul 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 313 0 07 Feb 2020