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Quantifying and Learning Linear Symmetry-Based Disentanglement
v1v2v3v4 (latest)

Quantifying and Learning Linear Symmetry-Based Disentanglement

International Conference on Machine Learning (ICML), 2020
11 November 2020
Loek Tonnaer
L. Rey
Vlado Menkovski
Mike Holenderski
J. Portegies
    FedMLCoGeDRL
ArXiv (abs)PDFHTML

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

4 / 4 papers shown
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
246
0
0
12 Jan 2023
Homomorphism Autoencoder -- Learning Group Structured Representations
  from Observed Transitions
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed TransitionsInternational Conference on Machine Learning (ICML), 2022
Hamza Keurti
Hsiao-Ru Pan
M. Besserve
Benjamin Grewe
Bernhard Schölkopf
AI4CE
354
22
0
25 Jul 2022
Equivariant Representation Learning via Class-Pose Decomposition
Equivariant Representation Learning via Class-Pose DecompositionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Giovanni Luca Marchetti
Gustaf Tegnér
Anastasiia Varava
Danica Kragic
DRL
347
18
0
07 Jul 2022
Linear Disentangled Representations and Unsupervised Action Estimation
Linear Disentangled Representations and Unsupervised Action Estimation
Matthew Painter
Jonathon S. Hare
Adam Prugel-Bennett
CoGeDRL
335
21
0
18 Aug 2020
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