1. MAML is one of the recently introduced and most commonly used meta learning algorithms, and it has lead to a major breakthrough in meta learning research. The basic idea of MAML is to find better initial parameters so that, with good initial parameters, the model can learn quickly on new tasks with fewer gradient steps.
2. MAML is model agnostic, meaning that we can apply MAML for any models that are trainable with gradient descent.
3. ADML is a variant of MAML that makes use of both clean and adversarial samples to find the better and robust initial model parameter, θ. 
4. In FGSM, we get the adversarial sample of our image and we calculate the gradients of our loss with respect to our image, more clearly input pixels of our image instead of the model parameter.
5. The context parameter is a task-specific parameter that's updated on the inner loop. It is denoted by ∅ and it is specific to each task and represents the embeddings of an individual task. 
6. The shared parameter is shared across tasks and updated in the outer loop to find the optimal model parameter. It is denoted by θ.