Vol. 4 No. 1 (2024): Advances in Deep Learning Techniques
Articles

Meta-learning Algorithms for Few-shot Learning: Analyzing meta-learning algorithms designed to enable deep learning models to quickly adapt to new tasks with limited training data

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  • Dr. Sofia Kovacs
Dr. Sofia Kovacs
Research Scientist in Healthcare Analytics, University of Warsaw, Poland
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Published 28-03-2024

Keywords

  • Meta-learning,
  • Few-shot learning,
  • Deep learning,
  • Meta-training,
  • Meta-testing,
  • MAML,
  • Reptile
    • ...More
    Less

How to Cite

[1]
D. S. Kovacs, “Meta-learning Algorithms for Few-shot Learning: Analyzing meta-learning algorithms designed to enable deep learning models to quickly adapt to new tasks with limited training data”, Adv. in Deep Learning Techniques, vol. 4, no. 1, pp. 58–66, Mar. 2024.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Meta-learning algorithms have gained significant attention in the field of deep learning for their ability to enable models to quickly adapt to new tasks with limited training data, a scenario known as few-shot learning. This paper provides an analysis of various meta-learning algorithms, focusing on their effectiveness in addressing the challenges of few-shot learning. We discuss the key concepts of meta-learning, including meta-training, meta-testing, and the use of task distributions, and review prominent algorithms such as MAML, Reptile, and ProtoNets. Additionally, we examine the applications of meta-learning in computer vision, natural language processing, and robotics, highlighting its potential for enhancing the adaptability of deep learning models in real-world scenarios. Through this analysis, we aim to provide insights into the current state of meta-learning research and its implications for future developments in few-shot learning.

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