Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pulling back information geometry   Latent space geometry has shown itself to provide a rich and rigorous
framework for interacting with the latent variables of deep generative models.
The existing theory, however, relies on the decoder being a Gaussian
distribution as its simple reparametrization allows us to interpret the
generating process as a random projection of a deterministic manifold.
Consequently, this approach breaks down when applied to decoders that are not
as easily reparametrized. We here propose to use the Fisher-Rao metric
associated with the space of decoder distributions as a reference metric, which
we pull back to the latent space. We show that we can achieve meaningful latent
geometries for a wide range of decoder distributions for which the previous
theory was not applicable, opening the door to `black box' latent geometries.

36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat