An Image Enhancement Method for Few-shot Classification

In order to predict the unknown image categories, few-shot image classification has recently become a very hot field. However, many methods need a large number of samples to support in order to achieve enough functions. This makes the whole network de amplification to meet a large number of effective feature extraction, and reduces the efficiency of few-shot classification to a certain extent. To solve these problems, we propose a dilate convolutional network with data enhancement. This network can not only meet the necessary features of image classification without increasing the number of samples, but also has a structure that utilizes a large number of effective features without sacrificing efficiency. The cutout structure can enhance the data by adding a fixed area 0 mask matrix in the process of image input. The structure of FAU uses dilate convolution and uses the characteristics of a sequence to improve the efficiency of the network.