Meta-Tasks: An alternative view on Meta-Learning Regularization

arXiv:2402.18599v1 Announce Type: new
Abstract: Few-shot learning (FSL) is a challenging machine learning problem due to a scarcity of labeled data. The ability to generalize effectively on both novel and training tasks is a significant barrier to FSL. This paper proposes a novel solution that can generalize to both training and novel tasks while also utilizing unlabeled samples. The method refines the embedding model before updating the outer loop using unsupervised techniques as ``meta-tasks''. The experimental results show that our …

abstract arxiv cs.ai cs.lg data few-shot few-shot learning machine machine learning meta meta-learning novel paper regularization solution tasks training type view