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Be More Active! Understanding the Differences between Mean and Sampled
  Representations of Variational Autoencoders

Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders

26 September 2021
Lisa Bonheme
M. Grzes
    DRL
ArXivPDFHTML

Papers citing "Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders"

5 / 5 papers shown
Title
How good are variational autoencoders at transfer learning?
How good are variational autoencoders at transfer learning?
Lisa Bonheme
M. Grzes
OOD
DRL
23
2
0
21 Apr 2023
Break The Spell Of Total Correlation In betaTCVAE
Break The Spell Of Total Correlation In betaTCVAE
Zihao Chen
Qiang Li
Bing Guo
CML
DRL
18
1
0
17 Oct 2022
FONDUE: an algorithm to find the optimal dimensionality of the latent
  representations of variational autoencoders
FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders
Lisa Bonheme
M. Grzes
DRL
28
6
0
26 Sep 2022
How do Variational Autoencoders Learn? Insights from Representational
  Similarity
How do Variational Autoencoders Learn? Insights from Representational Similarity
Lisa Bonheme
M. Grzes
CoGe
SSL
DRL
27
10
0
17 May 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
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