A Few Shot Classification Methods Based on Multiscale Relational Networks

Learning information from a single or a few samples is called few-shot learning. This learning method will solve deep learning's dependence on a large sample. Deep learning achieves few-shot learning through meta-learning: how to learn by using previous experience. Therefore, this paper considers how the deep learning method uses meta-learning to learn and generalize from a small sample size in image classification. The main contents are as follows. Practicing learning in a wide range of tasks enables deep learning methods to use previous empirical knowledge. However, this method is subject to the quality of feature extraction and the selection of measurement methods supports set and the target set. Therefore, this paper designs a multi-scale relational network (MSRN) aiming at the above problems. The experimental results show that the simple design of the MSRN can achieve higher performance. Furthermore, it improves the accuracy of the datasets within fewer samples and alleviates the overfitting situation. However, to ensure that uniform measurement applies to all tasks, the few-shot classification based on metric learning must ensure the task set's homologous distribution.

Automated summary

This paper introduces the concept of few-shot learning and how deep learning methods use meta-learning for few-shot learning. By designing a multi-scale relational network (MSRN), the performance of image classification in small sample scenarios can be improved, and the issue of overfitting can be alleviated.