Abstract: Deep learning methods have achieved great success in tasks like image classification, object detection and fault diagnosis. However, practical limitations often prevent gathering large amounts of data. Hence, there is a recent focus on algorithms for learning with small samples. This review aims to explain popular small sample learning methods and how they perform in real world applications. The review covers different approaches like metric models, memory models, parameter updating models and sample augmentation models, discussing their pros and cons. It also explores how these methods are applied in tasks like image classification, object detection, semantic segmentation and fault diagnosis. Lastly, it discusses the limitations of small sample learning methods and predicts future research trends focusing on less data dependency, more efficient algorithms and robust models.

Key words: deep learning, few shot learning, metric learning, image classification, object detection