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| Semi-supervised GAN× | Variationaler Autoencoder× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016 | 2014 |
| Urheber≠ | Odena, A.; Salimans, T. et al. | Kingma, D. P. & Welling, M. |
| Typ≠ | Semi-supervised generative model | Deep generative latent-variable model (encoder–decoder) |
| Wegweisende Quelle≠ | 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. link ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Aliasnamen | SGAN, Semi-GAN, semi-supervised generative adversarial network, GAN-based semi-supervised learning | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | 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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