Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in continual learning are mostly confined to a supervised learning setting, especially in NLP domain. In this work, we consider a few-shot continual active...
Active learningFew-shot learningText classificationContinual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in continual learning are mostly confined to a supervised learning setting, especially in NLP domain. In this ...
Learning algorithms for active learning, in ICML, 2017. P. Bachman, A. Sordoni, and A. Trischl...
例如在远程监督（Distant Supervision）的噪音训练数据上如何实现Few-Shot Learning：Tianyu Gao, Xu Han,...
We additionally evaluate both methods within the paradigms of "out of the box" active and continual learning. These settings were first proposed by Requeima et al., and studies how well few-shot classifiers, trained for few-shot learning, can be deployed for active and continual learning withou...
Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in continual learning are mostly confined to a supervised learning setting, especially in NLP domain. In this work, we consider a few-shot continual active...
Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot