Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Generative Models with Visual Attention   Attention has long been proposed by psychologists as important for
effectively dealing with the enormous sensory stimulus available in the
neocortex. Inspired by the visual attention models in computational
neuroscience and the need of object-centric data for generative models, we
describe for generative learning framework using attentional mechanisms.
Attentional mechanisms can propagate signals from region of interest in a scene
to an aligned canonical representation, where generative modeling takes place.
By ignoring background clutter, generative models can concentrate their
resources on the object of interest. Our model is a proper graphical model
where the 2D Similarity transformation is a part of the top-down process. A
ConvNet is employed to provide good initializations during posterior inference
which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our
model can robustly attend to face regions of novel test subjects. More
importantly, our model can learn generative models of new faces from a novel
dataset of large images where the face locations are not known.

10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph