Pareto Self-Supervised Training for Few-Shot Learning

• Published in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 13658 - 13667
• Main Authors: Chen, Zhengyu, Ge, Jixie, Zhan, Heshen, Huang, Siteng, Wang, Donglin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2021
• Subjects: Benchmark testing; Computer vision; Pareto optimization; Pattern recognition; Space exploration; Task analysis; Training

While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the complementarity of these two manners, few-shot auxiliary learning has recently draw...