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-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art.