… Meta-Learning and/or few-shot learning showed promising results, where they can learn or
… In this paper, we perform a study of the existing few-shot meta-learning techniques in the …

… capability of learning and generalizing from very few samples … booming deep learning, few
surveys for few sample learning (… provide a timely and comprehensive survey for FSL. In this …

… a few samples with supervised information. In this article, we conduct a thorough survey to
… we distinguish FSL from several relevant machine learning problems. We then point out that …

… labeled samples. First, instead of separating … to few training samples is categorized according
to the learning paradigm, including transfer learning, active learning, and few-shot learning…

… learning paradigms based on few samples or even cross-domain scenarios. Few-shot learning
(FSL), as well as meta-learning… in lighting conditions and scarce defect samples. In fact, …

… Additional memory modules are added to the model to remember the characteristic information
of a few samples, to complete the few-shot learning task. The workflow is shown in Fig. 8. …

… We discuss the challenges faced by the current small sample learning research progress
and propose possible directions for future research in Section 5. Finally, conclusions are drawn …

… few samples [88], or new samples become less similar [123]. One way to generalize unseen
samples directly from few examples is by learning … a few samples to quickly adapt and learn …

… , in this paper we survey key SSL techniques for deep learning of small sample in biomedical
… Can we just select the few most valuable samples for annotations and train deep models …

… In this survey, we have comprehensively reviewed over 90 … for two major sample shortage
settings—zero-shot learning (ZSL) … sample shortage settings: zero-shot learning (ZSL) and few-…