Generalizing from a Few Examples: A Survey on Few-shot Learning
Abstract
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this article, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience; (ii) model, which uses prior knowledge to reduce the size of the hypothesis space; and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications, and theories, are also proposed to provide insights for future research. 1
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In other research domains, few-shot learning has been shown to tune a pre-trained neural network and improve its generalization.
“However, in other research domains, it was shown [39] that a few-shot learning can be used to tune a pre-trained neural network and it will improve its generalization.”
From Improving Generalization of Deepfake Detection With Data Farming and Few-Shot Learning (2022) · cites this paper as [39]
Wei et al. showed that soft-prompt tuning can relax the non-degeneracy assumptions needed for downstream recovery, making it easier to extract task-specific information.
“Wei et al. [141] showed that soft-prompt tuning can relax the non-degeneracy assumptions (the generation probability of each token is linearly independent) needed for downstream recovery (i.e., recover the ground-truth labels of the downstream task.).making it easier to extract task-specific information.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [141]
This gap can result in overfitting, where the model excels on training data but underperforms on testing data, compromising its generalization ability.
“This gap can result in overfitting, where the model excels on training data but underperforms on testing data, compromising its generalization ability [27, 39].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [27]
When supervised information in D_train is sufficient, empirical risk minimization function in F can approximate the best-expected risk minimization function in F well.
“As illustrated in Fig. 4(a), when the supervised information in D train is sufficient, i.e., I in D train is large enough, the empirical risk minimization function in F can approximate the best-expected risk minimization function in F well, i.e., f e can provide a good approximation to f b .”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [27]
Empirical risk minimization does not ensure strong generalization on test data, especially with limited training samples.
“This approach doesn't ensure strong generalization on test data, especially with limited training samples.”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [27]
Existing FSL surveys do not touch upon the issue of FSCIL.
“Existing FSL surveys primarily focus on tackling ML problems with limited data. While they provide systematic classifications of FSL methods, they do not touch upon the issue of FSCIL.”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [27]
There is a clear lack of systematic and comprehensive surveys specifically on FSCIL.
“Despite existing surveys on FSL [27, 28, 29, 30] and IL [5, 6, 7, 11, 31, 32, 33, 34, 35], there is a clear lack of systematic and comprehensive surveys specifically on FSCIL.”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [27]
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- Authors
- Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- 4Paradigm Inc., Kwun Tong, Kowloon, Hong Kong
- Venue
- ACM Computing Surveys
- Published
- 2020-06-12
- Volume
- 53
- Issue
- 3
- Pages
- 1-34
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3034942609
- Cited by
- 3,165
- References
- 57
- Citations by year
- 2026: 332 · 2025: 596 · 2024: 625 · 2023: 612 · 2022: 504 · 2021: 382 · 2020: 106 · 2019: 8
- FWCI
- 157.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- few-shot learning, small datasets, prior knowledge, data augmentation
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