Meta-transfer learning for few-shot learning

• Main Authors: SUN, Qianru, LIU, Yaoyao, CHUA, Tat-Seng, SCHIELE, Bernt
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Few-shot learning; meta-learning; hard task mining; object classification; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4447; https://ink.library.smu.edu.sg/context/sis_research/article/5450/viewcontent/Sun_Meta_Transfer_Learning_for_Few_Shot_Learning_CVPR_2019_paper.pdf
• Institution: Singapore Management University

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural net...