零射击适应用于反向问题的扩散模型的近似后面采样.
Yaşar Utku Alçalar1, Mehmet Akçakaya1
1University of Minnesota, Minneapolis.
概括
零射击近似后部采样 (ZAPS) 加快了对反向问题的扩散模型推断. 这种方法提高了重建质量和对不规则噪声时间表的稳定性,减少了计算时间.
科学领域:
- 计算机成像成像技术
- 机器学习用于反向问题.
- 生成型模型是一种生成型模型.
背景情况:
- 扩散模型对于反向问题非常强大,但其推断速度很慢.
- 复杂的噪声时间表提高了扩散模型的性能,但对逆问题具有挑战性.
研究的目的:
- 开发一种方法,加速扩散模型推理反向问题.
- 在反向问题中提高扩散模型的稳定性和重建质量.
主要方法:
- 拟议的零射击近似后部采样 (ZAPS),是一种物理驱动的深度学习方法.
- ZAPS使用零射击训练与物理导向损失来学习不规则时间步骤的日志概率权重.
- 通过可学习的对角化方法来提高效率,近似了先前对数的赫西安.
主要成果:
- 与基线方法相比,ZAPS显著减少了推断时间.
- 在各种反向问题上证明了改进的重建质量,例如消除模糊,涂装和超分辨率.
- 展示了先进的扩散模型固有的不规则噪声时间表的稳定性.
结论:
- 在反向问题解决中,ZAPS为加速扩散模型提供了一个计算高效和有效的解决方案.
- 该方法提高了扩散模型在成像和其他科学领域的实际应用性.
- ZAPS提供了一个灵活的框架,可以适应各种后端采样扩散模型.
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