用Grad-CAM实时筛选数据，设备端训练省39%存储
DRIP: DRop unImportant data Points -- Enhancing Machine Learning Efficiency with Grad-CAM-Based Real-Time Data Prioritization for On-Device Training
- 基于Grad-CAM计算数据点重要性得分，动态决定保留或丢弃
- 在四个数据集上达到全量训练精度，存储节省最高达39%
- 适合资源受限的嵌入式设备，支持在线决策无需全量数据
数据点选择对机器学习至关重要。有效的选择方法可减少标注成本，优化嵌入式系统中数据存储有限场景下的本地训练，并提升模型性能。本文提出一种新算法，利用Grad-CAM实现在线决策，判断是否保留或丢弃数据点。该算法专为嵌入式设备设计，通过计算独特的DRIP Score量化每个数据点的重要性，从而动态决定是否存储以备重训，或直接丢弃而不影响模型表现。在四个基准数据集上的实验表明，该方法可在保持甚至超越全量数据训练模型精度的同时，实现最高达39%的存储节省。据我们所知，这是首个无需访问完整数据集即可进行在线数据点留存决策的算法。
原文摘要 · Abstract (English)
Selecting data points for model training is critical in machine learning. Effective selection methods can reduce the labeling effort, optimize on-device training for embedded systems with limited data storage, and enhance the model performance. This paper introduces a novel algorithm that uses Grad-CAM to make online decisions about retaining or discarding data points. Optimized for embedded devices, the algorithm computes a unique DRIP Score to quantify the importance of each data point. This enables dynamic decision-making on whether a data point should be stored for potential retraining or discarded without compromising model performance. Experimental evaluations on four benchmark datasets demonstrate that our approach can match or even surpass the accuracy of models trained on the entire dataset, all while achieving storage savings of up to 39\%. To our knowledge, this is the first algorithm that makes online decisions about data point retention without requiring access to the entire dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。