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弱监督扩散模型

弱监督扩散模型利用粗略、有噪声或不完整的监督信号（例如图像级类别标签、边界框或众包标注）来训练或调节去噪扩散概率模型，而不是使用像素级的精确真值。这使得在标注稀缺、完全标注不可行或成本过高的情况下，也能获得高质量的生成和判别输出。

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弱监督扩散模型
扩散模型生成对抗网络自监督扩散模型半监督扩散模型变分自编码器弱监督语义分割

来源

  1. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗
  2. Zhou, K., et al. (2023). Weakly-supervised Semantic Segmentation with Diffusion Models. arXiv preprint arXiv:2309.11803. link ↗

如何引用本页

ScholarGate. (2026, June 3). Weakly Supervised Diffusion Model (Denoising Diffusion with Imperfect Supervision). ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-diffusion-model

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ScholarGate — Weakly Supervised Diffusion Model (Weakly Supervised Diffusion Model (Denoising Diffusion with Imperfect Supervision)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/weakly-supervised-diffusion-model · 数据集： https://doi.org/10.5281/zenodo.20539026