Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Class Hierarchies with Metric-Guided Prototype Learning   In many classification tasks, the set of target classes can be organized into
a hierarchy. This structure induces a semantic distance between classes, and
can be summarised under the form of a cost matrix, which defines a finite
metric on the class set. In this paper, we propose to model the hierarchical
class structure by integrating this metric in the supervision of a prototypical
network. Our method relies on jointly learning a feature-extracting network and
a set of class prototypes whose relative arrangement in the embedding space
follows an hierarchical metric. We show that this approach allows for a
consistent improvement of the error rate weighted by the cost matrix when
compared to traditional methods and other prototype-based strategies.
Furthermore, when the induced metric contains insight on the data structure,
our method improves the overall precision as well. Experiments on four
different public datasets - from agricultural time series classification to
depth image semantic segmentation - validate our approach.

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The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci