在自然环境中以原则的黑盒知识蒸方式对准逻辑
IEEE transactions on pattern analysis and machine intelligence
|August 25, 2025
概括
黑盒知识蒸 (B2KD) 通过仅使用API查询来实现保护隐私的模型压缩. 映射模拟KD (MEKD) 增强了对未知数据分布的边缘模型的稳定性.
科学领域:
- 人工智能
- 机器学习
- 计算机视觉
背景情况:
- 黑盒知识蒸 (B2KD) 对于云对边的模型压缩至关重要,保护数据隐私和模型版权.
- B2KD仅使用教师模型API查询结果来提炼边缘设备的轻量级学生模型.
- 挑战包括有限的互联网交换和边缘云数据分布差异.
研究的目的:
- 通过提出从逻辑到细胞边界的新型优化方向来解决B2KD的挑战.
- 为强大的边缘模型预测和防干扰能力引入映射模拟KD (MEKD).
- 在各种未知数据分布的边缘设备上增强学生模型性能.
主要方法:
- 一个三阶段的工作流程:剥夺 (模拟反向教师映射),蒸 (通过高维图像点距离对齐低维逻辑) 和适应 (基于图形传播的测试时间适应).
- MEKD模拟了教师的反向功能,
- 通过最小化高维图像表示之间的距离来实现低维逻辑对齐.
主要成果:
- MEKD在各种教师学生架构的边缘模型蒸和适应方面表现出强的表现.
- 通过使用多种深度神经网络模型实现了多种图像识别基准.
- 该方法具有实用价值,特别是在遥感图像识别应用中.
结论:
- MEKD有效地提高了边缘部署的学生模型的稳定性和防干扰能力.
- 拟议的方法克服了B2KD中直接逻辑对齐的局限性.
- 在现实世界的边缘人工智能场景中,MEKD提供了保护隐私的实用和有效的模型压缩解决方案.
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