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

Single image-level annotations only correctly describe an often small subset of an image's
content, particularly when complex real-world scenes are depicted. While this might be …

Abstract Model-agnostic meta-learning (MAML) is one of the most successful meta-learning
algorithms. It has a bi-level optimization structure where the outer-loop process learns a …

Bi-level optimization, especially the gradient-based category, has been widely used in the
deep learning community including hyperparameter optimization and meta-knowledge …

In this paper, we hypothesize that gradient-based meta-learning (GBML) implicitly
suppresses the Hessian along the optimization trajectory in the inner loop. Based on this …

Modern deep learning has many drawbacks, including a heavy reliance on labeled data.
One of the key strategies for solving this problem is few-shot learning (FSL). With just a few …

Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a
principled framework that empowers learning a new task especially when data records are …

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning
algorithms. It has a bi-level optimization structure, where the outer-loop process learns the …