Addressing the Topological Defects of Disentanglement via Distributed Operators

10 February 2021

Papers citing "Addressing the Topological Defects of Disentanglement via Distributed Operators"

7 / 7 papers shown

Title
Cross-Entropy Is All You Need To Invert the Data Generating Process Patrik Reizinger Alice Bizeul Attila Juhos Julia E. Vogt Randall Balestriero Wieland Brendel David Klindt SSL OOD BDL DRL 102 3 0 29 Oct 2024
CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder Heeseung Jung Jaehyoung Jeong Kangil Kim CoGe 34 0 0 17 Jan 2024
Robust Self-Supervised Learning with Lie Groups Mark Ibrahim Diane Bouchacourt Ari S. Morcos SSL OOD 42 6 0 24 Oct 2022
Topographic VAEs learn Equivariant Capsules Thomas Anderson Keller Max Welling BDL 44 38 0 03 Sep 2021
Towards Building A Group-based Unsupervised Representation Disentanglement Framework Tao Yang Xuanchi Ren Yuwang Wang W. Zeng Nanning Zheng CoGe DRL 24 27 0 20 Feb 2021
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
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen Mario Geiger Maurice Weiler MLT AI4CE 165 309 0 05 Nov 2018