I've been trying to understand the meta-learning paradigm, more precisely, the optimization-based models, such as MAML, but I have a hard time understanding how I should correctly split my data to train such models.
For example, let's consider we have 4 traffic datasets, and we would like to use 3 of them as source datasets (to train the model) and the remaining one as target (to fine-tune on it and then test the model performance). As far as I understood, for each source dataset, I need to split it into train and validation. During training, I would randomly select 2 batches of samples from the training datasets, use one batch to compute the task-specific parameters and the other one to compute the loss. Then repeat the same process with the validation dataset, such that I can select the best candidate model. After the training is done, I need to fine-tune the model on the target dataset. I assume the process is the same, but I need to use the target dataset instead.
During testing (after the model is fully learned and fine-tuned), how exactly do I test it? It is the same procedure as if I was training a supervised model? I would like to know if the setup I described is correct and it fits the meta-learning paradigm.