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像素蒸:跨图像大小和异质网络的成本灵活蒸

Guangyu Guo, Dingwen Zhang, Longfei Han

IEEE transactions on pattern analysis and machine intelligence

|July 1, 2024

概括

像素蒸通过压缩输入图像和网络架构来增强模型部署. 这种方法为需要高效的图像处理和传输的系统提供了灵活的资源控制.

科学领域:

计算机科学计算机科学
人工智能的人工智能
机器学习机器学习

背景情况:

现有的知识蒸 (KD) 方法主要集中在网络架构压缩上.
这种方法对于与图像大小相关的部署成本是不够的,例如传输带宽和成像设备.

研究的目的:

引入像素蒸,一种新的方法,将KD扩展到输入级.
通过调整网络架构和图像质量来实现部署中的灵活成本控制.

主要方法:

输入空间表示蒸 (ISRD) 机制将空间知识从大图像转移到学生模型.
教师-助理-学生 (TAS) 框架将像素蒸分解为模型和输入压缩阶段.
调整维护特征 (AFP) 策略通过调整特征尺寸来调整像素蒸以进行对象检测.

主要成果:

像素蒸实现了模型部署的灵活成本控制.
ISRD机制促进了卷积神经网络 (CNN) 和视觉转换器 (ViT) 之间的稳定知识传输.
该TAS框架简化了像素蒸的复杂性和蒸中间知识的困难.

结论:

像素蒸通过解决架构和输入层面的模型压缩提供了一个全面的方法.
该方法在图像分类和物体检测任务中表现出显著的有效性.
这种技术为更高效和更适应性地部署深度学习模型提供了途径.

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