  Integrating knowledge across different domains is an essential feature of
human learning. Learning paradigms such as 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 and their comparative analysis. The weakness of one learning
algorithm turns out to be a strength of another, and thus, merging them is a
prevalent trait in the literature. Numerous research papers focus on each of
these learning paradigms separately and provide a comprehensive overview of
them. However, this article reviews research studies that combine (two of)
these learning algorithms. This survey describes how these techniques are
combined to solve problems in many different fields of research, including
computer vision, natural language processing, hyper-spectral imaging, and many
more, in a supervised setting only. Based on the knowledge accumulated from the
literature, we hypothesize a generic task-agnostic and model-agnostic learning
network - an ensemble of meta learning, transfer learning, and multi-task
learning, termed Multi-modal Multi-task Meta Transfer Learning. We also present
some open research questions, limitations, and future research directions for
this proposed network. The aim of this article is to spark interest among
scholars in effectively merging existing learning algorithms with the intention
of advancing research in this field. Instead of presenting experimental
results, we invite readers to explore and contemplate techniques for merging
algorithms while navigating through their limitations.
