Learning to self-train for semi-supervised few-shot classification

• Main Authors: LI, Xinzhe, SUN, Qianru, LIU, Yaoyao, ZHENG, Shibao, ZHOU, Qin, CHUA, Tat-Seng, SCHIELE, Bernt
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Few-shot learning; semi-supervised learning; meta-learning; image classification; Artificial Intelligence and Robotics; Computer Sciences; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4445; https://ink.library.smu.edu.sg/context/sis_research/article/5448/viewcontent/NeurIPS_2019_semi_supervised_camera_ready.pdf
• Institution: Singapore Management University

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta...