Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Convolutional Inverse Graphics Network   This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a
model that learns an interpretable representation of images. This
representation is disentangled with respect to transformations such as
out-of-plane rotations and lighting variations. The DC-IGN model is composed of
multiple layers of convolution and de-convolution operators and is trained
using the Stochastic Gradient Variational Bayes (SGVB) algorithm. We propose a
training procedure to encourage neurons in the graphics code layer to represent
a specific transformation (e.g. pose or light). Given a single input image, our
model can generate new images of the same object with variations in pose and
lighting. We present qualitative and quantitative results of the model's
efficacy at learning a 3D rendering engine.

9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci