Machine learningDeep learning / NLP / CV
自监督扩散模型
自监督扩散模型将去噪扩散概率模型的迭代加噪-去噪生成过程与自监督表示学习目标（如对比学习或掩码预测损失）相结合，从而使模型能够同时学习生成逼真的数据并产生语义上有意义的表示，而无需任何标记示例。
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来源
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗
- Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 1597–1607. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Diffusion Model (Denoising Diffusion with Self-supervised Representation Learning). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-diffusion-model
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