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| Semi-supervised Variational Autoencoder× | Generative Adversarial Network× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr | 2014 | 2014 |
| Urheber≠ | Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D. | Goodfellow, I. et al. |
| Typ≠ | Generative probabilistic model (semi-supervised) | Generative deep learning (adversarial two-network game) |
| Wegweisende Quelle≠ | 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 ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Aliasnamen | Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | 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. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
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