Meta-learning tries to learn meta-knowledge from a large number of tasks. However, the
stochastic meta-gradient can have large variance due to data sampling (from each task) and …

The success of meta-learning on existing benchmarks is predicated on the assumption that
the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of the …

How to rapidly adapt to new tasks and improve model generalization through few-shot
learning remains a significant challenge in meta-learning. Model-Agnostic Meta-Learning …

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 …

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on
prior experience. Gradient (or optimization) based meta-learning has recently emerged as …

Gradient based meta-learning methods are prone to overfit on the meta-training set, and this
behaviour is more prominent with large and complex networks. Moreover, large networks …

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 aims to extract common knowledge from similar training tasks in order to
facilitate efficient and effective learning on future tasks. Several recent works have extended …

Inspired by optimization techniques, we propose a novel meta-learning algorithm with
gradient modulation to encourage fast-adaptation of neural networks in the absence of …