I have two cases for label training samples by using active learning:
Case 1: I would apply active learning to query a small chunk of samples gradually to label them and my model is being trained during this process. After a certain number of iterations, I have a training dataset with specific performance of the model.
Case 2: I re-train the model from scratch with the training dataset in case 1.
Question 1(regarding training): do you think the performance of the model will be the same in both cases? why, please?
Question 2(regarding testing): do you think there is a difference to apply active learning to label the testing dataset after case 1 or apply it after case 2?