扩散模型和代表性学习:一项调查
IEEE transactions on pattern analysis and machine intelligence
|January 29, 2026
概括
这项调查探讨了扩散模型和表示学习,突出了它们与自我监督学习的联系. 它详细介绍了在识别任务中使用扩散模型的方法,并通过表现学习的进步来改进扩散模型.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 扩散模型是视觉任务中突出的生成方法.
- 它们作为自我监督的学习技术而起作用,不需要标记数据.
- 它们与代表性学习的协同作用是研究的一个关键领域.
研究的目的:
- 调查扩散模型和表示学习之间的关系.
- 为它们的相互作用提供全面的分类学.
- 确定当前的挑战和未来的研究方向.
主要方法:
- 扩散模型基础的概述:数学基础,网络架构和指导策略.
- 详细检查利用预训练的传播模型进行识别的框架.
- 探索利用表示和自我监督学习增强传播模型的方法.
主要成果:
- 建立了连接扩散模型和表示学习的分类学.
- 从扩散模型转移学习的突出框架.
- 展示了应用到扩散模型的自我监督学习的进步.
结论:
- 扩散模型和表示学习具有显著和日益增长的相互作用.
- 进一步的研究可以弥合现有的差距,并探索新的应用.
- 这项调查为该领域提供了基础资源.
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