Meta-transfer learning through hard tasks

• Main Authors: SUN, Qianru, LIU, Yaoyao, CHEN, Zhaozheng, CHUA Tat-Seng, SCHIELE Bernt
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: few-shot learning; transfer learning; meta learning; image classification; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5591; https://ink.library.smu.edu.sg/context/sis_research/article/6594/viewcontent/1910.03648.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...