Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Approach to Addressing Zero-Shot Learning Problem   In recent years, self-supervised learning has had significant success in
applications involving computer vision and natural language processing. The
type of pretext task is important to this boost in performance. One common
pretext task is the measure of similarity and dissimilarity between pairs of
images. In this scenario, the two images that make up the negative pair are
visibly different to humans. However, in entomology, species are nearly
indistinguishable and thus hard to differentiate. In this study, we explored
the performance of a Siamese neural network using contrastive loss by learning
to push apart embeddings of bumblebee species pair that are dissimilar, and
pull together similar embeddings. Our experimental results show a 61% F1-score
on zero-shot instances, a performance showing 11% improvement on samples of
classes that share intersections with the training set.

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The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
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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: funct-an
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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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: ao-sci
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The categories of this paper are: patt-sol