Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches
<|reference_start|>Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches: 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.<|reference_end|>
@article{tsoumplekas2024toward,
    title={Toward Green and Human-Like Artificial Intelligence: A Complete Survey
  on Contemporary Few-Shot Learning Approaches},
    author={Georgios Tsoumplekas, Vladislav Li, Panagiotis Sarigiannidis,
  Vasileios Argyriou},
    journal={arXiv preprint arXiv:2402.03017},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03017},
    primaryClass={cs.LG cs.AI}
}