The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck

• URL: http://arxiv.org/abs/2211.07959v1
• Date: Tue, 15 Nov 2022 07:44:56 GMT
• Title: The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck
• Authors: Chen Shani, Jonathan Zarecki, Dafna Shahaf
• Abstract summary: Machine learning (ML) is revolutionizing the world, affecting almost every field of science and industry. Recent algorithms are increasingly data-hungry, requiring large datasets for training. However, obtaining quality datasets of such magnitude proves to be a difficult challenge.
• Score: 16.18460753647167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) is revolutionizing the world, affecting almost every field of science and industry. Recent algorithms (in particular, deep networks) are increasingly data-hungry, requiring large datasets for training. Thus, the dominant paradigm in ML today involves constructing large, task-specific datasets. However, obtaining quality datasets of such magnitude proves to be a difficult challenge. A variety of methods have been proposed to address this data bottleneck problem, but they are scattered across different areas, and it is hard for a practitioner to keep up with the latest developments. In this work, we propose a taxonomy of these methods. Our goal is twofold: (1) We wish to raise the community's awareness of the methods that already exist and encourage more efficient use of resources, and (2) we hope that such a taxonomy will contribute to our understanding of the problem, inspiring novel ideas and strategies to replace current annotation-heavy approaches.

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