Deep learning

Metric (unit)

Sample (material)

Overfitting

Meta learning (computer science)

Data mining

Machine learning

Engineering

Artificial intelligence

Systems engineering

Set (abstract data type)

Chemistry

Programming language

Operations management

Computer science

Task (project management)

Chromatography

Artificial neural network

Applied Sciences

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.

A Few Shot Classification Methods Based on Multiscale Relational Networks