Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DynamicVAE: Decoupling Reconstruction Error and Disentangled
  Representation Learning   This paper challenges the common assumption that the weight $\beta$, in
$\beta$-VAE, should be larger than $1$ in order to effectively disentangle
latent factors. We demonstrate that $\beta$-VAE, with $\beta < 1$, can not only
attain good disentanglement but also significantly improve reconstruction
accuracy via dynamic control. The paper removes the inherent trade-off between
reconstruction accuracy and disentanglement for $\beta$-VAE. Existing methods,
such as $\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence
term in the objective function, leading to high reconstruction errors for the
sake of better disentanglement. To mitigate this problem, a ControlVAE has
recently been developed that dynamically tunes the KL-divergence weight in an
attempt to control the trade-off to more a favorable point. However, ControlVAE
fails to eliminate the conflict between the need for a large $\beta$ (for
disentanglement) and the need for a small $\beta$. Instead, we propose
DynamicVAE that maintains a different $\beta$ at different stages of training,
thereby decoupling disentanglement and reconstruction accuracy. In order to
evolve the weight, $\beta$, along a trajectory that enables such decoupling,
DynamicVAE leverages a modified incremental PI (proportional-integral)
controller, and employs a moving average as well as a hybrid annealing method
to evolve the value of KL-divergence smoothly in a tightly controlled fashion.
We theoretically prove the stability of the proposed approach. Evaluation
results on three benchmark datasets demonstrate that DynamicVAE significantly
improves the reconstruction accuracy while achieving disentanglement comparable
to the best of existing methods. The results verify that our method can
separate disentangled representation learning and reconstruction, removing the
inherent tension between the two.

34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an