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| 준지도 학습 변이형 오토인코더× | 준지도 학습 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2014 | 2018–2019 |
| 창시자≠ | Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D. | Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community |
| 유형≠ | Generative probabilistic model (semi-supervised) | Semi-supervised deep learning |
| 원전≠ | Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589. link ↗ | 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. DOI ↗ |
| 별칭 | Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model | semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model |
| 관련≠ | 6 | 5 |
| 요약≠ | The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations. | 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. |
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