In this repository, I've utilized the Model-Agnostic Meta-Learning Algorithm (MAML) for few-shot image classification tasks on the Mini-ImageNet dataset. The MAML technique allows the model to quickly adapt to various tasks, facilitating rapid learning and adaptation.
MAML enables models to learn versatile strategies applicable across multiple tasks rather than being limited to specific functions. Imagine a model that can efficiently handle diverse tasks, picking up general tricks that benefit various scenarios—it's the essence of MAML.
The objective revolves around numerous tasks grouped as sets of batches, denoted as p(T), which this model must adeptly handle. The aim for the model's continual improvement in tackling new tasks without fixating too much on a singular approach. Thus, our focus lies in discovering optimal model settings that significantly impact the model's performance when faced with a new task from p(T).