• Corpus ID: 221655276

Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations

  title={Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations},
  author={Ze Cheng and Juncheng Li and Chenxu Wang and Jixuan Gu and Hao Xu and Xinjian Li and Florian Metze},
In the problem of learning disentangled representations, one of the promising methods is to factorize aggregated posterior by penalizing the total correlation of sampled latent variables. However, this well-motivated strategy has a blind spot: there is a disparity between the sampled latent representation and its corresponding mean representation. In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of… 

Perioperative Predictions with Interpretable Latent Representation

The results show that the latent representation provided by cVAE leads to superior performance in classification, regression and multi-task predictions, and the interpretability of the disentangled representation and its capability to capture intrinsic characteristics of hospitalized patients.

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This paper extends the existing definition of mean representations to mean representations and shows that active variables are equally disentangled in both representations, and isolates the passive variables, which show that they are responsible for the discrepancies between mean and sampled representations.



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3d shapes dataset

  • https://github.com/deepmind/3dshapes-dataset/.
  • 2018

Elbo surgery: yet another way to carve up the variational evidence

  • 2016

Isolating Sources of Disentanglement in Variational Autoencoders

We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation