Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying individual facial expressions by deconstructing a neural
  network   This paper focuses on the problem of explaining predictions of psychological
attributes such as attractiveness, happiness, confidence and intelligence from
face photographs using deep neural networks. Since psychological attribute
datasets typically suffer from small sample sizes, we apply transfer learning
with two base models to avoid overfitting. These models were trained on an age
and gender prediction task, respectively. Using a novel explanation method we
extract heatmaps that highlight the parts of the image most responsible for the
prediction. We further observe that the explanation method provides important
insights into the nature of features of the base model, which allow one to
assess the aptitude of the base model for a given transfer learning task.
Finally, we observe that the multiclass model is more feature rich than its
binary counterpart. The experimental evaluation is performed on the 2222 images
from the 10k US faces dataset containing psychological attribute labels as well
as on a subset of KDEF images.

2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th