中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning

文献类型：期刊论文

作者Lai, Nan3,4; Kan, Meina3,4; Han, Chunrui3,4; Song, Xingguang1; Shan, Shiguang2,3,4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-08-01
卷号32期号:8页码:3458-3470
关键词Task analysis Adaptation models Training Predictive models Feature extraction Generators Computational modeling Few-shot learning meta-learning predict classifier weights task-adaptive predictor
ISSN号2162-237X
DOI10.1109/TNNLS.2020.3011526
英文摘要Few-shot learning aims to learn a well-performing model from a few labeled examples. Recently, quite a few works propose to learn a predictor to directly generate model parameter weights with episodic training strategy of meta-learning and achieve fairly promising performance. However, the predictor in these works is task-agnostic, which means that the predictor cannot adjust to novel tasks in the testing phase. In this article, we propose a novel meta-learning method to learn how to learn task-adaptive classifier-predictor to generate classifier weights for few-shot classification. Specifically, a meta classifier-predictor module, (MPM) is introduced to learn how to adaptively update a task-agnostic classifier-predictor to a task-specialized one on a novel task with a newly proposed center-uniqueness loss function. Compared with previous works, our task-adaptive classifier-predictor can better capture characteristics of each category in a novel task and thus generate a more accurate and effective classifier. Our method is evaluated on two commonly used benchmarks for few-shot classification, i.e., miniImageNet and tieredImageNet. Ablation study verifies the necessity of learning task-adaptive classifier-predictor and the effectiveness of our newly proposed center-uniqueness loss. Moreover, our method achieves the state-of-the-art performance on both benchmarks, thus demonstrating its superiority.
资助项目National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China[61772496] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2017145]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000681169500019
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17407]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Kan, Meina
作者单位1.Huawei Technol Co Ltd, Shenzhen 518129, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Lai, Nan,Kan, Meina,Han, Chunrui,et al. Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(8):3458-3470.
APA Lai, Nan,Kan, Meina,Han, Chunrui,Song, Xingguang,&Shan, Shiguang.(2021).Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(8),3458-3470.
MLA Lai, Nan,et al."Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.8(2021):3458-3470.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。