Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Disentangling Autoencoders (DAE)   Noting the importance of factorizing (or disentangling) the latent space, we
propose a novel, non-probabilistic disentangling framework for autoencoders,
based on the principles of symmetry transformations in group-theory. To the
best of our knowledge, this is the first deterministic model that is aiming to
achieve disentanglement based on autoencoders without regularizers. The
proposed model is compared to seven state-of-the-art generative models based on
autoencoders and evaluated based on five supervised disentanglement metrics.
The experimental results show that the proposed model can have better
disentanglement when variances of each features are different. We believe that
this model leads to a new field for disentanglement learning based on
autoencoders without regularizers.

17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat