Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Multi-task Learning for Facial Expression Recognition and Synthesis
  Based on Selective Feature Sharing   Multi-task learning is an effective learning strategy for deep-learning-based
facial expression recognition tasks. However, most existing methods take into
limited consideration the feature selection, when transferring information
between different tasks, which may lead to task interference when training the
multi-task networks. To address this problem, we propose a novel selective
feature-sharing method, and establish a multi-task network for facial
expression recognition and facial expression synthesis. The proposed method can
effectively transfer beneficial features between different tasks, while
filtering out useless and harmful information. Moreover, we employ the facial
expression synthesis task to enlarge and balance the training dataset to
further enhance the generalization ability of the proposed method. Experimental
results show that the proposed method achieves state-of-the-art performance on
those commonly used facial expression recognition benchmarks, which makes it a
potential solution to real-world facial expression recognition problems.

22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex