Learning to teach and learn for semi-supervised few-shot image classification

• Main Authors: LI, Xinzhe, HUANG, Jianqiang, LIU, Yaoyao, ZHOU, Qin, ZHENG, Shibao, SCHIELE, Bernt, SUN, Qianru
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Few-shot learning; Meta-learning; Semi-supervised learning; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6628; https://ink.library.smu.edu.sg/context/sis_research/article/7631/viewcontent/1_s20_S1077314221001144_main.pdf
• Institution: Singapore Management University

This paper presents a novel semi-supervised few-shot image classification method named Learning to Teach and Learn (LTTL) to effectively leverage unlabeled samples in small-data regimes. Our method is based on self-training, which assigns pseudo labels to unlabeled data. However, the conventional ps...