I am trying to a semi-supervised learning.
I first train on the small set of training set (represented by training on the test set, instead of normal training set), then during validation, I take the predictions with the probability higher than 0.9, label them (this is yet to be done, so I just take the given label for now).
Then I take the data from the test_dataset that has more than 0.9 prediction probability, then I what to append/add to the original train_set, then feed this new train_set to train the model.