Certainly! Model-Agnostic Meta-Learning (MAML) is a technique proposed in the paper 'Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks' by Chelsea Finn et al. It addresses the challenge of learning from limited data by learning an initialization of parameters that can be easily fine-tuned to new tasks. This allows for rapid adaptation to new tasks with minimal examples. MAML has gained significant attention in the field of deep networks as it enables effective generalization and efficient learning in contexts where data scarcity is a concern.

Model-Agnostic Meta-Learning (MAML) is a framework that aims to enable effective generalization across different learning tasks. In the context of deep networks, MAML allows for rapid adaptation to new tasks with minimal data and computational resources. It does this by learning an effective initialization that can be fine-tuned using only a few examples. The significance of MAML lies in its ability to address the challenge of data scarcity and enable efficient learning and adaptation in complex domains.

MAML, introduced in the paper 'Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks' by Chelsea Finn et al., is a meta-learning algorithm that enables fast adaptation of deep networks to new tasks. It achieves this by learning an initialization of parameters that allows for quick adaptation to new tasks with limited data. MAML has shown promising results in applications where data availability is limited, enabling efficient learning and adaptation in tasks such as image classification, reinforcement learning, and few-shot learning.