Depending on the nature of this knowledge and how a meta-learner utilizes this information on a new task, two types of meta-learning can be distinguished: optimization-based and embedding-based meta-learning. In optimization-based meta-learning, the model is trained to adapt quickly to the new task with a small number of training samples. Model agnostic meta-learning (MAML) is a prominent example of the optimization-based method, where the experience is an initialization of the neural network learned from a distribution of related tasks. 17 After determining the prototype for the new example (x) in the query set, the probability of the label is assigned based on the distance of the sample from all the prototypes.