Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Ensemble with Shared Representations Based on Convolutional Networks
  for Continually Learning Facial Expressions   Social robots able to continually learn facial expressions could
progressively improve their emotion recognition capability towards people
interacting with them. Semi-supervised learning through ensemble predictions is
an efficient strategy to leverage the high exposure of unlabelled facial
expressions during human-robot interactions. Traditional ensemble-based
systems, however, are composed of several independent classifiers leading to a
high degree of redundancy, and unnecessary allocation of computational
resources. In this paper, we proposed an ensemble based on convolutional
networks where the early layers are strong low-level feature extractors, and
their representations shared with an ensemble of convolutional branches. This
results in a significant drop in redundancy of low-level features processing.
Training in a semi-supervised setting, we show that our approach is able to
continually learn facial expressions through ensemble predictions using
unlabelled samples from different data distributions.

3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
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The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci