Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Puss-uzraudzītā difūzijas modelis× | Daudzpusīgā apguve× | |
|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2020–2022 | 1970s–2006 (formalized) |
| Autors≠ | Multiple groups (Ho et al., Song et al., and successors) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tips≠ | Generative model with semi-supervised guidance | Learning paradigm |
| Pirmavots≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Citi nosaukumi | Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGateDatu kopa ↗ |
|
|