Improving graph prototypical network using active learning

TL;DR: In this article , the authors used few-shot learning with active learning and also used the power of graph convolutional networks in classifying data with a graphical structure, and they have tested their proposed model in the field of electronic commerce for tagging goods in big online stores, which encounter a large number of diverse products.

Abstract: Abstract Due to the growth of using various devices and applications in modern life, the amount of data available is skyrocketing, but labeling all of this data is beyond the reach of data scientists. Thus, it is necessary to categorize data with a small amount of labeled data. In fact, it should be possible to prioritize data for labeling. To achieve this goal in this study, we have used few-shot learning with active learning and also used the power of graph convolutional networks in classifying data with a graphical structure. To implement the proposed model, we use two graph convolutional networks in parallel to calculate the embedding and the importance of each node. Using the output of both networks, we create prototypes of classes, and then, we classify them according to the distance of each node of these prototypes. We have also used active learning to select data more intelligently, which improves the overall model performance. As well as this, we have tested our proposed model in the field of electronic commerce for tagging goods in big online stores, which encounter a large number of diverse products, where high accuracy categorization in a short time without the interference of human factor and with the help of artificial intelligence is needed to reduce costs. The results of implementing the model on the Amazon dataset and its comparison with the state-of-the-art models in this field show the superiority of our method.