Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Dive into Semi-Supervised ELBO for Improving Classification
  Performance   Decomposition of the evidence lower bound (ELBO) objective of VAE used for
density estimation revealed the deficiency of VAE for representation learning
and suggested ways to improve the model. In this paper, we investigate whether
we can get similar insights by decomposing the ELBO for semi-supervised
classification using VAE model. Specifically, we show that mutual information
between input and class labels decreases during maximization of ELBO objective.
We propose a method to address this issue. We also enforce cluster assumption
to aid in classification. Experiments on a diverse datasets verify that our
method can be used to improve the classification performance of existing VAE
based semi-supervised models. Experiments also show that, this can be achieved
without sacrificing the generative power of the model.

20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org