Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Interpolations in Deep Generative Models under Non-Simply-Connected
  Manifold Topology   Exploiting the deep generative model's remarkable ability of learning the
data-manifold structure, some recent researches proposed a geometric data
interpolation method based on the geodesic curves on the learned data-manifold.
However, this interpolation method often gives poor results due to a
topological difference between the model and the dataset. The model defines a
family of simply-connected manifolds, whereas the dataset generally contains
disconnected regions or holes that make them non-simply-connected. To
compensate this difference, we propose a novel density regularizer that make
the interpolation path circumvent the holes denoted by low probability density.
We confirm that our method gives consistently better interpolation results from
the experiments with real-world image datasets.

2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg