Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks Regularization for Structured Output Prediction   A deep neural network model is a powerful framework for learning
representations. Usually, it is used to learn the relation $x \to y$ by
exploiting the regularities in the input $x$. In structured output prediction
problems, $y$ is multi-dimensional and structural relations often exist between
the dimensions. The motivation of this work is to learn the output dependencies
that may lie in the output data in order to improve the prediction accuracy.
Unfortunately, feedforward networks are unable to exploit the relations between
the outputs. In order to overcome this issue, we propose in this paper a
regularization scheme for training neural networks for these particular tasks
using a multi-task framework. Our scheme aims at incorporating the learning of
the output representation $y$ in the training process in an unsupervised
fashion while learning the supervised mapping function $x \to y$.
  We evaluate our framework on a facial landmark detection problem which is a
typical structured output task. We show over two public challenging datasets
(LFPW and HELEN) that our regularization scheme improves the generalization of
deep neural networks and accelerates their training. The use of unlabeled data
and label-only data is also explored, showing an additional improvement of the
results. We provide an opensource implementation
(https://github.com/sbelharbi/structured-output-ae) of our framework.

34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
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
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph