A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning

• URL: http://arxiv.org/abs/2103.00845v1
• Date: Mon, 1 Mar 2021 08:31:28 GMT
• Title: A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning
• Authors: Huimin Peng
• Abstract summary: This paper briefly reviews the connections between meta-learning and self-supervised learning. We show that an integration of meta-learning and self-supervised learning models can best contribute to the improvement of model generalization capability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper briefly reviews the connections between meta-learning and self-supervised learning. Meta-learning can be applied to improve model generalization capability and to construct general AI algorithms. Self-supervised learning utilizes self-supervision from original data and extracts higher-level generalizable features through unsupervised pre-training or optimization of contrastive loss objectives. In self-supervised learning, data augmentation techniques are widely applied and data labels are not required since pseudo labels can be estimated from trained models on similar tasks. Meta-learning aims to adapt trained deep models to solve diverse tasks and to develop general AI algorithms. We review the associations of meta-learning with both generative and contrastive self-supervised learning models. Unlabeled data from multiple sources can be jointly considered even when data sources are vastly different. We show that an integration of meta-learning and self-supervised learning models can best contribute to the improvement of model generalization capability. Self-supervised learning guided by meta-learner and general meta-learning algorithms under self-supervision are both examples of possible combinations.

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