Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for
  Low-Resource Real-World Data   Most of the current supervised automatic music transcription (AMT) models
lack the ability to generalize. This means that they have trouble transcribing
real-world music recordings from diverse musical genres that are not presented
in the labelled training data. In this paper, we propose a semi-supervised
framework, ReconVAT, which solves this issue by leveraging the huge amount of
available unlabelled music recordings. The proposed ReconVAT uses
reconstruction loss and virtual adversarial training. When combined with
existing U-net models for AMT, ReconVAT achieves competitive results on common
benchmark datasets such as MAPS and MusicNet. For example, in the few-shot
setting for the string part version of MusicNet, ReconVAT achieves F1-scores of
61.0% and 41.6% for the note-wise and note-with-offset-wise metrics
respectively, which translates into an improvement of 22.2% and 62.5% compared
to the supervised baseline model. Our proposed framework also demonstrates the
potential of continual learning on new data, which could be useful in
real-world applications whereby new data is constantly available.

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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con