LibFewShot: A Comprehensive Library for Few-Shot Learning-Reference-Cited by-同舟云学术

LibFewShot: A Comprehensive Library for Few-Shot Learning

Published:2023-12 Issue:12 Volume:45 Page:14938-14955
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Li Wenbin¹^ORCID,Wang Ziyi¹^ORCID,Yang Xuesong¹^ORCID,Dong Chuanqi¹^ORCID,Tian Pinzhuo²^ORCID,Qin Tiexin³^ORCID,Huo Jing¹^ORCID,Shi Yinghuan¹^ORCID,Wang Lei⁴^ORCID,Gao Yang¹^ORCID,Luo Jiebo⁵^ORCID

Affiliation:

1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

2. School of Computer Engineering and Science, Shanghai University, Shanghai, China

3. Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

4. School of Computing and Information Technology, University of Wollongong, Wollongong, Australia

5. Department of Computer Science, University of Rochester, Rochester, NY, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Collaborative Innovation Center of Novel Software Technology and Industrialization

Jiangsu Provincial Double-Innovation Doctor Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10308548/10239698.pdf?arnumber=10239698

Reference70 articles.

1. Meta-Transfer Learning for Few-Shot Learning

2. Boosting Few-Shot Visual Learning With Self-Supervision

3. Meta-Learning With Differentiable Convex Optimization

4. Bag of tricks for adversarial training;pang;Proc Int Conf Learn Representations,2021

5. Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning

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