Hi @SimonW
I am trying to implement model-agnostic meta learning  https://arxiv.org/abs/1703.03400 24
There is already a really nice reference implementation (for the supervised learning case) in pytorch, but I am trying to see if I can do it another way.
The basic idea is that there is a set of base weights, and one set that are adapted to a specific task (by means of a gradient step). Gradients are needed either w.r.t the base parameters or the adapted parameters.
Thanks for the replies, atleast I think I am on the right track.