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| 준지도 학습 변이형 오토인코더× | Variational Autoencoder× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2014 | 2014 |
| 창시자≠ | Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D. | Kingma, D. P. & Welling, M. |
| 유형≠ | Generative probabilistic model (semi-supervised) | Deep generative latent-variable model (encoder–decoder) |
| 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 ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| 별칭 | Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
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