Tamas Nyiri and Attila Kiss
What Can We Learn from Small Data
Over the past decade, deep learning has profoundly transformed the landscape of science and technology, from refining advertising algorithms to pioneering self-driving vehicles. While advancements in computational capabilities have fueled this evolution, the consistent availability of high quality training data is less of a given. In this work, the authors aim to provide a bird’s eye view on topics pertaining to small data scenarios, that is scenarios in which a less than desirable quality and quantity of data is given for supervised learning. We provide an overview for a set of challenges, proposed solution and at the end tie it together by practical guidelines on which techniques are useful in specific real-world scenarios.
Please cite this paper the following way:
Tamas Nyiri and Attila Kiss, "What Can We Learn from Small Data ", Infocommunications Journal, Special Issue on Applied Informatics, 2023, pp. 27-34, https://doi.org/10.36244/ICJ.2023.5.5