Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

5 June 2020

Papers citing "Evaluating the Disentanglement of Deep Generative Models through Manifold Topology"

9 / 9 papers shown

Title
Towards Scalable Topological Regularizers Hiu-Tung Wong Darrick Lee Hong Yan BDL 59 0 0 24 Jan 2025
Disentanglement Learning via Topology Nikita Balabin Daria Voronkova I. Trofimov Evgeny Burnaev S. Barannikov DRL 58 2 0 24 Aug 2023
Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams) Mathieu Pont Julien Tierny 26 3 0 05 Jul 2023
Representation Topology Divergence: A Method for Comparing Neural Network Representations S. Barannikov I. Trofimov Nikita Balabin Evgeny Burnaev 3DPC 28 45 0 31 Dec 2021
Activation Landscapes as a Topological Summary of Neural Network Performance Matthew Wheeler Jose J. Bouza Peter Bubenik 31 19 0 19 Oct 2021
On Disentangled Representations Learned From Correlated Data Frederik Trauble Elliot Creager Niki Kilbertus Francesco Locatello Andrea Dittadi Anirudh Goyal Bernhard Schölkopf Stefan Bauer OOD CML 29 115 0 14 Jun 2020
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 279 10,354 0 12 Dec 2018
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 188 273 0 03 Dec 2018
Statistical topological data analysis using persistence landscapes Peter Bubenik 106 846 0 27 Jul 2012