ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2011.06070
  4. Cited By
Quantifying and Learning Linear Symmetry-Based Disentanglement

Quantifying and Learning Linear Symmetry-Based Disentanglement

11 November 2020
Loek Tonnaer
L. Rey
Vlado Menkovski
Mike Holenderski
J. Portegies
    FedML
    CoGe
    DRL
ArXivPDFHTML

Papers citing "Quantifying and Learning Linear Symmetry-Based Disentanglement"

3 / 3 papers shown
Title
Equivariant Representation Learning in the Presence of Stabilizers
Equivariant Representation Learning in the Presence of Stabilizers
Luis Armando
∗. GiovanniLucaMarchetti
Danica Kragic
D. Jarnikov
Mike Holenderski
29
0
0
12 Jan 2023
Equivariant Representation Learning via Class-Pose Decomposition
Equivariant Representation Learning via Class-Pose Decomposition
G. Marchetti
Gustaf Tegnér
Anastasiia Varava
Danica Kragic
DRL
35
14
0
07 Jul 2022
Weakly-Supervised Disentanglement Without Compromises
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
1