Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Forced to Learn: Discovering Disentangled Representations Without
  Exhaustive Labels   Learning a better representation with neural networks is a challenging
problem, which was tackled extensively from different prospectives in the past
few years. In this work, we focus on learning a representation that could be
used for a clustering task and introduce two novel loss components that
substantially improve the quality of produced clusters, are simple to apply to
an arbitrary model and cost function, and do not require a complicated training
procedure. We evaluate them on two most common types of models, Recurrent
Neural Networks and Convolutional Neural Networks, showing that the approach we
propose consistently improves the quality of KMeans clustering in terms of
Adjusted Mutual Information score and outperforms previously proposed methods.

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The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg