MAML is one of the recently introduced and most popularly used meta learning algorithms and it has created a major breakthrough in meta learning research. Learning to learn is the key focus of meta learning and we know that, in meta learning, we learn from various related tasks containing only a small number of data points and the meta learner produces a quick learner that can generalize well on a new related task even with a lesser number of training samples.
The basic idea of MAML is to find a better initial parameter so that, with good initial parameters, the model can learn quickly on new tasks with fewer gradient steps.
So, what do we mean by that? Let's say we are performing a classification task using a neural network. How do we train the network? We will start off with initializing random weights and train the network by minimizing the loss. How do we minimize...
