Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI   Data scarcity and noise are important issues in industrial applications of
machine learning. However, it is often challenging to devise a scalable and
generalized approach to address the fundamental distributional and semantic
properties of dataset with black box models. For this reason, data-centric
approaches are crucial for the automation of machine learning operation
pipeline. In order to serve as the basis for this automation, we suggest a
domain-agnostic pipeline for refining the quality of data in image
classification problems. This pipeline contains data valuation, cleansing, and
augmentation. With an appropriate combination of these methods, we could
achieve 84.711% test accuracy (ranked #6, Honorable Mention in the Most
Innovative) in the Data-Centric AI competition only with the provided dataset.

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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: comp-gas