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  2. 基于多目标进化神经架构的轻量级扩散模型
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  2. 基于多目标进化神经架构的轻量级扩散模型

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基于多目标进化神经架构的轻量级扩散模型

Yu Xue1, Chunxiao Jiao1, Yong Zhang2

  • 1School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China.

International journal of neural systems
|August 29, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

我们开发了基于多目标进化搜索 (LDMOES) 的轻量级扩散模型, 以创建高效的扩散模型. 在保持或提高图像生成质量的同时,LDMOES显著降低了计算成本.

关键词:
轻量级的扩散模型知识的蒸多目标进化算法神经架构搜索

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科学领域:

  • 人工智能
  • 计算机视觉
  • 机器学习

背景情况:

  • 扩散模型在图像生成方面表现出色,但其计算成本高,推断时间长.
  • 现有的加速方法主要集中在推断步骤上,忽视扩散模型架构优化.
  • 优化扩散模型架构对于开发计算高效的生成模型至关重要.

研究的目的:

  • 提出LDMOES (基于多目标进化搜索的轻量级扩散模型),这是设计高效的基于UNet的扩散模型的新框架.
  • 利用多目标进化神经架构搜索和知识蒸来优化扩散模型架构.
  • 在不影响图像生成质量的情况下降低扩散模型的计算复杂性.

主要方法:

  • 实现了一个框架,将多目标进化神经架构的搜索与知识蒸结合起来.
  • 在LDMOES中使用模块化搜索空间来解架构组件并提高搜索效率.
  • 在CIFAR-10,Tiny-ImageNet,CelebA-HQ和LSUN-church等多种数据集上验证了拟议的方法.

主要成果:

  • LDMOES在像素空间中的多重积累操作 (MAC) 减少了约40%,超过了教师模型.
  • 在Tiny-ImageNet数据集上,该模型产生了高质量的图像,具有4.16的竞争性FID分数,显示出强烈的概括性.
  • 在隐藏空间中,MAC减少了50%的性能损失,在LSUN-church上,计算成本减少了近60%.
  • 结论:

    • 通过多目标进化搜索和知识提炼,LDMOES有效设计了轻量级和高效的基于UNet的传播模型.
    • 拟议的方法在像素和隐藏空间中显著降低了计算成本 (MAC),同时保持或提高了生成质量.
    • LDMOES在各种数据集中表现出强大的有效性和可转移性,为高效的生成人工智能提供了有希望的方向.