A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

• URL: http://arxiv.org/abs/2205.06743v1
• Date: Fri, 13 May 2022 16:24:35 GMT
• Title: A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
• Authors: Yisheng Song, Ting Wang, Subrota K Mondal, Jyoti Prakash Sahoo
• Abstract summary: Few-shot learning has emerged as an effective learning method and shows great potential. We extensively investigated 200+ latest papers on FSL published in the past three years. We propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge.
• Score: 5.809416101410813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of the strengths and weaknesses of the existing works. For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of FSL. To enrich this survey, in each subsection we provide in-depth analysis and insightful discussion about recent advances on these topics. Moreover, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into the technology evolution trends together with potential future research opportunities in the hope of providing guidance to follow-up research.

Related papers

• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey [67.48187503803847]
  Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm. Recent research has shown promising results addressing various challenges in VFL. This survey offers a systematic overview of recent developments.
  arXiv  Detail & Related papers  (2024-05-25T16:05:06Z)
• Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches [6.078001259817318]
  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.
  arXiv  Detail & Related papers  (2024-02-05T13:55:54Z)
• Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects [84.36935309169567]
  We present a broad review of recent advances for fine-grained analysis in zero-shot learning (ZSL) We first provide a taxonomy of existing methods and techniques with a thorough analysis of each category. Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library.
  arXiv  Detail & Related papers  (2024-01-31T11:51:24Z)
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark [55.898771405172155]
  Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. We provide a systematic overview of the important and recent developments of research on federated learning.
  arXiv  Detail & Related papers  (2023-11-12T06:32:30Z)
• A Survey on Few-Shot Class-Incremental Learning [11.68962265057818]
  Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks. This paper provides a comprehensive survey on FSCIL. FSCIL has achieved impressive achievements in various fields of computer vision.
  arXiv  Detail & Related papers  (2023-04-17T10:15:08Z)
• Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison [54.357707168883024]
  Few-Shot Object Detection (FSOD) mimics the humans' ability of learning to learn. FSOD intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. We give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols.
  arXiv  Detail & Related papers  (2022-03-27T04:11:28Z)
• Self-supervised on Graphs: Contrastive, Generative,or Predictive [25.679620842010422]
  Self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks. We divide existing graph SSL methods into three categories: contrastive, generative, and predictive. We also summarize the commonly used datasets, evaluation metrics, downstream tasks, and open-source implementations of various algorithms.
  arXiv  Detail & Related papers  (2021-05-16T03:30:03Z)
• Learning from Very Few Samples: A Survey [80.06120185496403]
  Few sample learning is significant and challenging in the field of machine learning. Few sample learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability.
  arXiv  Detail & Related papers  (2020-09-06T06:13:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.