| Paper ID | C-3-3.4 |
| Paper Title |
ADAPTIVE MULTI-PROTOTYPE RELATION NETWORK |
| Authors |
Xiaoxu Li, Tao Tian, Lanzhou University of Technology, China; Yuxin Liu, The University of Melbourne, Australia; Hong Yu, Ludong University, China; Jie Cao, Lanzhou University of Technology, China; Zhanyu Ma, Beijing University of Posts and Telecommunications, China |
| Session |
C-3-3: Machine Learning for Small-sample Data Analysis |
| Time | Thursday, 10 December, 17:30 - 19:30 |
| Presentation Time: | Thursday, 10 December, 18:15 - 18:30 Check your Time Zone |
|
All times are in New Zealand Time (UTC +13) |
| Topic |
Machine Learning and Data Analytics (MLDA): Special Session: Machine Learning for Small-sample Data Analysis |
| Abstract |
Based on Relation Network, we propose a new network structure that can adaptively adjust the number of prototypes according to data distribution. Our method, called the Adaptive Multi-prototype Relation Network(AMRN), aims at extracting more reasonable prototype representation for different data distribution in few-shot learning case. Instead of representing each class as a single prototype in the relational network, we represent each class with one or more prototypes, and solve the problem of embedding network with the relational network connection, which can improving the classification accuracy in few-shot learning. Besides, our method can easily extend to other network structures, which is also a useful reference for other metric learning approaches. |