The ability to learn new concepts with small amounts of data is a critical aspect of
intelligence that has proven challenging for deep learning methods. Meta-learning has …

Meta-learning algorithms aim to learn two components: a model that predicts targets for a
task, and a base learner that updates that model when given examples from a new task. This …

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 …

Meta-learning methods have shown an impressive ability to train models that rapidly learn
new tasks. However, these methods only aim to perform well in expectation over tasks …

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 …

Despite the broad interest in meta-learning, the generalization problem remains one of the
significant challenges in this field. Existing works focus on meta-generalization to unseen …

Meta-learning methods have been extensively studied and applied in computer vision,
especially for few-shot classification tasks. The key idea of meta-learning for few-shot …

The constant introduction of standardized benchmarks in the literature has helped
accelerating the recent advances in meta-learning research. They offer a way to get a fair …

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-
learning practice, it can have high computational cost because it updates all model …

Biased regularization and fine tuning are two recent meta-learning approaches. They have
been shown to be effective to tackle distributions of tasks, in which the tasks' target vectors …