what about difference between the meta learning and semi-supervised learning and self-supervised learning and active learning and federated learning and few-shot learning? in application and in definition? Pros and cons?

What are the differences between zero-shot , one-shot , few-shot learning? and what about their difference in usage/ application? fields of their application? Comparisons of their Pros & Cons?

I used to believe in k-way-n-shot few-shot learning, k and n (number of classes and samples from each class respectively) must be the same in train and test phases. But now I come across a git repository for few-shot learning that uses different numbers in the train and test phase : parser.add_argument('--dataset') parser.add_argument('--distance', default='l2') parser.add_argument('--n-train', default=1, type=int) parser.add_argument('--n-test', default=1, type=int) parser.add_argument('--k-train', default=60, type=int) parser.add_argument('--k-test', default=5, type=int) parser.add_argument('--q-train', default=5, type=int) parser.add_argument('--q-test', default=1, type=int) Are we allowed to do so?