Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Factorizing Aggregated Posterior in Learning Disentangled
  Representations   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 the mean
representation. Indeed, we prove that for the multivariate normal
distributions, the mean representation with arbitrarily high total correlation
can have a corresponding sampled representation with bounded total correlation.
We also propose a method to eliminate this disparity. Experiments show that our
model can learn a mean representation with much lower total correlation, hence
a factorized mean representation. Moreover, we offer a detailed explanation of
the limitations of factorizing aggregated posterior: factor disintegration. Our
work indicates a potential direction for future research of disentangled
learning.

19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin