Sharing to learn and learning to share - Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning : A meta review
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Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms like transfer learning, meta learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms along with a comparative analysis. The weakness of a learning algorithm turns out to be the strength of another, and thereby merging them is a prevalent trait in the literature. This work delivers a literature review of the articles, which fuses two algorithms to accomplish multiple tasks. A global generic learning network, an ensemble of meta learning, transfer learning, and multi-task learning, is also introduced here, along with some open research questions and directions for future research.
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