Vertaile menetelmiä
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| Heikosti ohjattu diffuusiomalli× | Generatiivinen kilpaileva verkko× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2022–2024 | 2014 |
| Kehittäjä≠ | Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024 | Goodfellow, I. et al. |
| Tyyppi≠ | Generative model with imperfect supervision | Generative deep learning (adversarial two-network game) |
| Alkuperäislähde≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Rinnakkaisnimet | WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Liittyvät≠ | 6 | 4 |
| Tiivistelmä≠ | A weakly supervised diffusion model trains or conditions a denoising diffusion probabilistic model using coarse, noisy, or incomplete supervision signals — such as image-level class labels, bounding boxes, or crowd-sourced annotations — instead of pixel-precise ground truth. This allows high-quality generative and discriminative outputs in annotation-scarce settings where full labeling is infeasible or prohibitively expensive. | 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. |
| ScholarGateAineisto ↗ |
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