Σύγκριση μεθόδων

	Ασθενώς Επιβλεπόμενο Transformer	Ημι-επιβλεπόμενος Μετασχηματιστής
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017–2019	2018–2019
Δημιουργός	Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017)	Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community
Τύπος	Weakly supervised deep learning	Semi-supervised deep learning
Θεμελιώδης πηγή	Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training data creation with weak supervision. Proceedings of the VLDB Endowment, 11(3), 269–282.	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.
Εναλλακτικές ονομασίες	WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers	semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model
Συναφείς	5	5
Σύνοψη	Weakly Supervised Transformer combines the representational power of Transformer architectures with weak supervision strategies that exploit noisy, incomplete, or programmatically generated labels — making it possible to train high-quality NLP and vision models when fully annotated datasets are scarce or prohibitively expensive to produce.	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.