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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