Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vāji uzraudzīts variācijas autoenkoders× | Generatīvais Adversariālais Tīkls× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2014–2018 | 2014 |
| Autors≠ | Kingma, D. P. et al. (building on VAE and semi-supervised deep generative models) | Goodfellow, I. et al. |
| Tips≠ | Generative model with weak supervision | Generative deep learning (adversarial two-network game) |
| Pirmavots≠ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR 2014). link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Citi nosaukumi | WS-VAE, weakly-supervised VAE, semi-supervised VAE with weak labels, label-guided variational autoencoder | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | A Weakly Supervised Variational Autoencoder (WS-VAE) extends the standard VAE generative framework by incorporating partial, noisy, or coarse supervision signals — such as crowd-sourced labels, heuristic rules, or programmatic annotations — to guide latent space learning without requiring fully annotated data. It is widely applied in computer vision, NLP, and biomedical domains where complete ground-truth labels are expensive or unavailable. | 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. |
| ScholarGateDatu kopa ↗ |
|
|