| Paper ID | C-3-3.6 |
| Paper Title |
ANTI-NOISE RELATION NETWORK FOR FEW-SHOT LEARNING |
| Authors |
Xiaoxu Li, Jintao Yan, Jijie Wu, Lanzhou University of Technology, China; Yuxin Liu, University of Melbourne, Australia; Xiaochen Yang, University College London, United Kingdom; 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, 18:45 - 19:00 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 |
Few-shot classification has received great attention in the field of machine learning and computer vision. Its aim is to achieve the learning ability close to human recognition by training from a few labelled samples. The existing few-shot classification methods have attempted to alleviate the impact of insufficient samples in a variety of ways, such as meta-learning and metric learning, but they ignore the noise robustness. This work proposes a new Anti-Noise Relation Network by embedding an autoencoder network into a classical neural network of few-shot classification, Relation Network. Experimental results on the Stanford Car and CUB-200-2011 datasets demonstrate the superiority of the proposed method in both classification accuracy and robustness against different noises. |