反时针方向的区块对区块的知识蒸用于神经网络压缩
Xiaowei Lan1, Yalin Zeng1, Xiaoxia Wei2
1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China.
Scientific reports
|April 2, 2025
概括
本研究介绍了逆时钟方向的区块智能知识蒸 (CBKD),这是改善模型压缩的知识蒸 (KD) 的新方法. CBKD增强了教师和学生模型之间的中间知识的转移,提高了绩效.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 模型压缩对于在资源有限的设备上部署大型神经网络至关重要.
- 知识蒸 (KD) 将知识从大型教师模型转移到较小的学生模型.
- 现有的KD方法通常使用一个或两个阶段,可能会限制知识传输.
研究的目的:
- 引入一种新的方法,反时钟方向的区块智能知识蒸 (CBKD),以优化知识蒸过程.
- 在知识转移过程中减轻教师和学生模型之间的代际差距.
- 为了促进中间层知识的传播.
主要方法:
- CBKD将教师和学生模型分为多个子网络块.
- 每个阶段,知识从一个教师子块转移到相应的学生子块.
- 更深层次的教师子网络块被赋予更高的压缩率.
主要成果:
- 在微型图像200和CIFAR-10数据集上进行了实验.
- 拟议的CBKD方法证明了提炼性能的提高.
- 通过CBKD改进了各种主流知识蒸方法.
结论:
- CBKD提供了一种优化知识蒸的有效策略.
- 区块式转移和差压缩率有助于改进模型压缩.
- 这种方法提高了神经网络中知识传输的效率和有效性.
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