Comments (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.
- 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.
Related Issues (13)
- SUNRGBD dataset HOT 1
- Visualization of sunrgbd and scannet HOT 1
- Does the size consistency loss only affect the size residual? HOT 2
- data ratio config
- How to visualize the test results?
- how to train with 100% label? HOT 2
- Question about Autonomous Driving Datasets HOT 2
- A Parameter Is Missing in SunrgbdDetectionVotesDataset HOT 1
- training other data sets HOT 3
- Some questions about Multi-GPUs HOT 1
- an error when eval the model HOT 3
- would it boost performence using non-labeled val data? HOT 2
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