Abstract
n 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
