Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Co-Representation Learning For Classification and Novel Class Detection
  via Deep Networks   One of the key challenges of performing label prediction over a data stream
concerns with the emergence of instances belonging to unobserved class labels
over time. Previously, this problem has been addressed by detecting such
instances and using them for appropriate classifier adaptation. The fundamental
aspect of a novel-class detection strategy relies on the ability of comparison
among observed instances to discriminate them into known and unknown classes.
Therefore, studies in the past have proposed various metrics suitable for
comparison over the observed feature space. Unfortunately, these similarity
measures fail to reliably identify distinct regions in observed feature spaces
useful for class discrimination and novel-class detection, especially in
streams containing high-dimensional data instances such as images and texts. In
this paper, we address this key challenge by proposing a semi-supervised
multi-task learning framework called \sysname{} which aims to intrinsically
search for a latent space suitable for detecting labels of instances from both
known and unknown classes. We empirically measure the performance of \sysname{}
over multiple real-world image and text datasets and demonstrate its
superiority by comparing its performance with existing semi-supervised methods.

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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin