方法对比
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| 弱监督扩散模型× | 半监督扩散模型× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2022–2024 | 2020–2022 |
| 提出者≠ | Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024 | Multiple groups (Ho et al., Song et al., and successors) |
| 类型≠ | Generative model with imperfect supervision | Generative model with semi-supervised guidance |
| 开创性文献≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗ | 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 ↗ |
| 别名 | WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training | Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion |
| 相关≠ | 6 | 3 |
| 摘要≠ | 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 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. |
| ScholarGate数据集 ↗ |
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