Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Class Zero-Shot Learning for Artistic Material Recognition   Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no
labelled examples of the data to be classified are provided during the training
stage. Instead, ZSL uses additional information learned about the domain, and
relies upon transfer learning algorithms to infer knowledge about the missing
instances. ZSL approaches are an attractive solution for sparse datasets. Here
we outline a model to identify the materials with which a work of art was
created, by learning the relationship between English descriptions of the
subject of a piece and its composite materials. After experimenting with a
range of hyper-parameters, we produce a model which is capable of correctly
identifying the materials used on pieces from an entirely distinct museum
dataset. This model returned a classification accuracy of 48.42% on 5,000
artworks taken from the Tate collection, which is distinct from the Rijksmuseum
network used to create and train our model.

27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga