Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Geometry of Deep Generative Image Models and its Applications   Generative adversarial networks (GANs) have emerged as a powerful
unsupervised method to model the statistical patterns of real-world data sets,
such as natural images. These networks are trained to map random inputs in
their latent space to new samples representative of the learned data. However,
the structure of the latent space is hard to intuit due to its high
dimensionality and the non-linearity of the generator, which limits the
usefulness of the models. Understanding the latent space requires a way to
identify input codes for existing real-world images (inversion), and a way to
identify directions with known image transformations (interpretability). Here,
we use a geometric framework to address both issues simultaneously. We develop
an architecture-agnostic method to compute the Riemannian metric of the image
manifold created by GANs. The eigen-decomposition of the metric isolates axes
that account for different levels of image variability. An empirical analysis
of several pretrained GANs shows that image variation around each position is
concentrated along surprisingly few major axes (the space is highly
anisotropic) and the directions that create this large variation are similar at
different positions in the space (the space is homogeneous). We show that many
of the top eigenvectors correspond to interpretable transforms in the image
space, with a substantial part of eigenspace corresponding to minor transforms
which could be compressed out. This geometric understanding unifies key
previous results related to GAN interpretability. We show that the use of this
metric allows for more efficient optimization in the latent space (e.g. GAN
inversion) and facilitates unsupervised discovery of interpretable axes. Our
results illustrate that defining the geometry of the GAN image manifold can
serve as a general framework for understanding GANs.

30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat