Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

This article reviews meta-learning also known as learning-to-learn which seeks rapid and
accurate model adaptation to unseen tasks with applications in highly automated AI, few …

Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Deep neural networks can achieve great successes when presented with large data sets
and sufficient computational resources. However, their ability to learn new concepts quickly …

Meta-learning, or learning to learn, is a machine learning approach that utilizes prior
learning experiences to expedite the learning process on unseen tasks. As a data-driven …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Meta-learning has proven to be a powerful paradigm for transferring the knowledge from
previous tasks to facilitate the learning of a novel task. Current dominant algorithms train a …

Meta-learning aims to extract meta-knowledge from historical tasks to accelerate learning on
new tasks. Typical meta-learning algorithms like MAML learn a globally-shared meta-model …

Deep neural networks excel in regimes with large amounts of data, but tend to struggle
when data is scarce or when they need to adapt quickly to changes in the task. In response …

Meta-learning enables algorithms to quickly learn a newly encountered task with just a few
labeled examples by transferring previously learned knowledge. However, the bottleneck of …