压缩和比较:在ML模型压缩实验中交互评估效率和行为
IEEE transactions on visualization and computer graphics
|September 10, 2024
概括
实践者经常难以比较机器学习模型压缩实验. 新的COMPRESS AND COMPARE系统提供了一个交互式视觉接口,以简化分析和更好地理解模型行为变化.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 数据可视化 数据可视化
背景情况:
- 在设备上部署机器学习模型需要压缩算法来减少模型大小并提高推断速度.
- 对压缩实验进行有效比较对于分析准确性-效率权衡和模型行为变化至关重要.
- 现有的工具往往使分析过程碎片化,导致效率低下和不完整的见解.
研究的目的:
- 开发一个交互式视觉系统,压缩和比较,支持现实世界的比较工作流程,用于机器学习模型压缩.
- 为可视化模型来源和比较预测,权重和激活提供统一的界面.
主要方法:
- 开发了互动视觉系统 COMPRESS 和 COMPARE.
- 通过对生成语言模型和图像分类模型的案例研究来证明系统的实用性.
- 通过与八名机器学习压缩专家的用户研究对系统的评估.
主要成果:
- COMPRESS AND COMPARE有效地可视化了压缩模型之间的来源关系.
- 该系统通过比较模型预测,权重和激活来揭示压缩诱导的行为变化.
- 用户研究表明,系统结构压缩工作流程,建立从业者的直觉,并鼓励彻底分析.
结论:
- 压缩和比较通过提供统一的视觉界面来增强机器学习模型压缩的分析.
- 该系统有助于调试压缩失败并识别压缩文物.
- 确定了特定于压缩的挑战,并为模型比较中的未来视觉分析工具提供了可概括的可视化.
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