Few-shot and meta-learning methods for image understanding: a survey

Abstract

Abstract State-of-the-art deep learning systems (e.g., ImageNet image classification) typically require very large training sets to achieve high accuracies. Therefore, one of the grand challenges is called few-shot learning where only a few training samples are required for good performance. In this survey, we illuminate one of the key paradigms in few-shot learning called meta-learning. These meta-learning methods, by simulating the tasks which will be presented at inference through episodic training, can effectively employ previous prior knowledge to guide the learning of new tasks. In this paper, we provide a comprehensive overview and key insights into the meta-learning approaches and categorize them into three branches according to their technical characteristics, namely metric-based, model-based and optimization-based meta-learning. Due to the major importance of the evaluation process, we also present an overview of current widely used benchmarks, as well as performances of recent meta-learning methods on these datasets. Based on over 200 papers in this survey, we conclude with the major challenges and future directions of few-shot learning and meta-learning.