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How small is big enough? Open labeled datasets and the development of deep learning

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  • Daniel Souza
  • Aldo Geuna
  • Jeff Rodríguez

Abstract

We investigate the emergence of Deep Learning as a technoscientific field, emphasizing the role of open labeled datasets. Through qualitative and quantitative analyses, we evaluate the role of datasets like Canadian Institute of Advanced Research - 10 classes (CIFAR-10) in advancing computer vision and object recognition, which are central to the Deep Learning revolution. Our findings highlight CIFAR-10’s crucial role and enduring influence on the field, as well as its importance in teaching ML techniques. Results also indicate that dataset characteristics such as size, number of instances, and number of categories, were key factors. Econometric analysis confirms that CIFAR-10, a small-but-sufficiently large open dataset, played a significant and lasting role in technological advancements and had a major function in the development of the early scientific literature as shown by citation metrics.

Suggested Citation

  • Daniel Souza & Aldo Geuna & Jeff Rodríguez, 2025. "How small is big enough? Open labeled datasets and the development of deep learning," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 34(6), pages 1322-1365.
    • Handle: RePEc:oup:indcch:v:34:y:2025:i:6:p:1322-1365.
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    File URL: http://hdl.handle.net/10.1093/icc/dtaf044
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    More about this item

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O35 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Social Innovation
    • H5 - Public Economics - - National Government Expenditures and Related Policies

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