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

Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of
tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse …

Despite deep learning's widespread success, its data-hungry and computationally
expensive nature makes it impractical for many data-constrained real-world applications …

The long-term goal of machine learning is to learn general visual representations from a
small amount of data without supervision, mimicking three advantages of human cognition: i) …

Unsupervised meta-learning aims to learn feature representations from unsupervised
datasets that can transfer to downstream tasks with limited labeled data. In this paper, we …