قارن الطرق

	المحولات شبه المُشرف عليها	شبكة عصبية التفافية شبه مُشرف عليها
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2018–2019	2013–2017
صاحب الطريقة	Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
النوع	Semi-supervised deep learning	Semi-supervised deep learning
المصدر التأسيسي	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.	Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.
الأسماء البديلة	semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
ذات صلة	5	5
الملخص	Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token prediction, then fine-tunes it on the labeled task. This two-stage approach dramatically reduces the labeled data needed to achieve strong performance.	A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.