Multi-scale Relation Network for Few-Shot Learning Based on Meta-learning

• Published in: Computer Vision Systems Vol. 11754; pp. 343 - 352
• Main Authors: Ding, Yueming, Tian, Xia, Yin, Lirong, Chen, Xiaobing, Liu, Shan, Yang, Bo, Zheng, Wenfeng
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Deep learning; Few-shot learning; Meta-learning; Multi-scales feature
• ISBN: 3030349942; 9783030349943
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-34995-0_31

Deep neural networks can learn a huge function space, because they have millions of parameters to fit large amounts of labeled data. However, this advantage is a major obstacle for few-shot learning, because which has to make predictions based on only few samples of each class. In this work, inspired by multi-scale features methods and relation network which uses neural network to learn metrics, we propose a concise and efficient network, multi-scale relation network. The network consists of a feature extractor and a metric learner. Firstly, the feature extractor extracts multi-scale features by combining features from different convolutional layers. Secondly, we generate the relation feature by calculating the absolute value of the difference between multi-scale features. The results on benchmark sets show that our method avoids the over fitting and elongates the period of learning process, providing higher performance with simple design choices.