Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches

• URL: http://arxiv.org/abs/2402.03017v1
• Date: Mon, 5 Feb 2024 13:55:54 GMT
• Title: Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches
• Authors: Georgios Tsoumplekas, Vladislav Li, Vasileios Argyriou, Anastasios Lytos, Eleftherios Fountoukidis, Sotirios K. Goudos, Ioannis D. Moscholios, Panagiotis Sarigiannidis
• Abstract summary: Few-Shot Learning aims to enable rapid adaptation to novel learning tasks. Recent trends shaping the field, outstanding challenges, and promising future research directions are discussed.
• Score: 6.078001259817318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite deep learning's widespread success, its data-hungry and computationally expensive nature makes it impractical for many data-constrained real-world applications. Few-Shot Learning (FSL) aims to address these limitations by enabling rapid adaptation to novel learning tasks, seeing significant growth in recent years. This survey provides a comprehensive overview of the field's latest advancements. Initially, FSL is formally defined, and its relationship with different learning fields is presented. A novel taxonomy is introduced, extending previously proposed ones, and real-world applications in classic and novel fields are described. Finally, recent trends shaping the field, outstanding challenges, and promising future research directions are discussed.

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