방법 비교

	준지도 학습 GAN	BERT 기반 준지도 학습 분류
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2016	2019–2020
창시자	Odena, A.; Salimans, T. et al.	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)
유형	Semi-supervised generative model	Semi-supervised fine-tuning of pre-trained transformer
원전	Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved Techniques for Training GANs. Advances in Neural Information Processing Systems (NeurIPS), 29.	Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.
별칭	SGAN, Semi-GAN, semi-supervised generative adversarial network, GAN-based semi-supervised learning	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning
관련	5	6
요약	Semi-supervised GAN (SGAN) extends the standard GAN discriminator to simultaneously classify labeled examples into K real classes and detect generated fakes as a (K+1)-th class, letting the generator's synthetic data act as implicit regularization and allowing strong classifiers to be trained with very few labeled examples.	Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.