Realization of Few-shot learning with a small amount of data by Graph Neural Network

Abstract

In the case of error detection of Web screens by the convolutional neural network of the previous research, it is necessary to label the training data of normal and error. In error detection on a Web screen, it is difficult to collect training data by assuming an error screen in advance because it is uncertain what kind of error will occur. In this study, we compared the images acquired in the past and calculated the similarity to detect errors probabilistically without labeling the training data. As a result, Few-shot learning is possible to emphasize the characteristics of the training data from less past data, and detect error candidates on the Web screen by link prediction of the graph neural network (GNN).