why pretrain? about sess HOT 1 CLOSED

1. a) Yes. The result training from scratch is not good. b) Pre-training the backbone with labeled data will make the following training easier, which is also suggested by original mean-teacher work. c) Our SESS is proposed for semi-supervised learning setting, where there always have a group of labeled samples. Of course, it would be interesting if we can use pretrained weights from other datasets, albeit some other problems such as domain adaptation might need to be solved.
2. a) Theoretically, if more unlabeled data follows a wider distribution, we should be able to achieve better performance. b) We did not conduct such an experiment. You are encouraged to explore if interested (Please let me know the result if you are willing to do this :P).

from sess.