Self-training is a wrapper method for semi-supervised learning. First, a supervised learning algorithm is trained based on the labeled data only. This classifier is then applied to the unlabeled data to generate more labeled examples as input for the supervised learning algorithm. One problem with self-training is that the First a supervised learning algorithm mistakes reinforce themselves.
if your data is continuous, k-means may turn out to be helpful.
https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhai_S4L_Self-Supervised_Semi-Supervised_Learning_ICCV_2019_paper.pdf
I suggest you follow https://www.ijert.org/a-comprehensive-study-and-analysis-of-semi-supervised-learning-techniques
https://jrodthoughts.medium.com/understanding-semi-supervised-learning-a6437c070c87
Article A semi-supervised system for weed mapping in sunflower crops...
Dear Hani Ayyoub ,
Look the link, maybe useful.
Conference Paper Self-Training using Selection Network for Semi-supervised Learning
Regards,
Shafagat
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