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| Halbüberwachtes Diffusionsmodell× | Variationaler Autoencoder× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2020–2022 | 2014 |
| Urheber≠ | Multiple groups (Ho et al., Song et al., and successors) | Kingma, D. P. & Welling, M. |
| Typ≠ | Generative model with semi-supervised guidance | Deep generative latent-variable model (encoder–decoder) |
| Wegweisende Quelle≠ | Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2256–2265. link ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Aliasnamen | Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-conditioned outputs while still exploiting the structure in unlabeled data. | 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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