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Learning Disentangled Representations with Reference-Based Variational
  Autoencoders

Learning Disentangled Representations with Reference-Based Variational Autoencoders

24 January 2019
Adria Ruiz
Oriol Martínez
Xavier Binefa
Jakob Verbeek
    OOD
    CoGe
    DRL
ArXivPDFHTML

Papers citing "Learning Disentangled Representations with Reference-Based Variational Autoencoders"

6 / 6 papers shown
Title
Separating common from salient patterns with Contrastive Representation
  Learning
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset
Edouard Duchesnay
Antoine Grigis
Pietro Gori
SSL
DRL
46
1
0
19 Feb 2024
Moment Matching Deep Contrastive Latent Variable Models
Moment Matching Deep Contrastive Latent Variable Models
Ethan Weinberger
Nicasia Beebe-Wang
Su-In Lee
23
16
0
21 Feb 2022
NestedVAE: Isolating Common Factors via Weak Supervision
NestedVAE: Isolating Common Factors via Weak Supervision
M. Vowels
Necati Cihan Camgöz
Richard Bowden
CML
DRL
26
21
0
26 Feb 2020
Theory and Evaluation Metrics for Learning Disentangled Representations
Theory and Evaluation Metrics for Learning Disentangled Representations
Kien Do
T. Tran
CoGe
DRL
18
93
0
26 Aug 2019
DualDis: Dual-Branch Disentangling with Adversarial Learning
DualDis: Dual-Branch Disentangling with Adversarial Learning
Thomas Robert
Nicolas Thome
Matthieu Cord
CoGe
DRL
25
4
0
03 Jun 2019
Disentangling Factors of Variation Using Few Labels
Disentangling Factors of Variation Using Few Labels
Francesco Locatello
Michael Tschannen
Stefan Bauer
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
DRL
CML
CoGe
29
123
0
03 May 2019
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