用图像蒸馏提升流数据分类精度，适合资源受限场景。
Enhancing Classification of Streaming Data with Image Distillation
- 通过蒸馏提取流数据关键特征，降低计算开销。
- 准确率达73.1%，优于霍夫丁树、自适应随机森林等方法。
- 适用于内存和算力有限的实时图像分类任务。
本研究针对内存与计算资源有限环境下流数据高效分类的挑战，探索数据蒸馏在图像流分类中的应用。通过从数据流中提炼核心特征，该方法在保持关键信息的同时显著降低计算负担。实验对比了传统算法如霍夫丁树（Hoeffding Trees）和自适应随机森林（Adaptive Random Forest），并引入嵌入表示处理图像数据。基于蒸馏的分类方法（DBC）取得73.1%的准确率，优于传统方法及基于水库采样的分类技术（RBC）。该成果在复杂数据流处理中展现了高精度与高效率，为流数据分类设立了新标准。
原文摘要 · Abstract (English)
This study tackles the challenge of efficiently classifying streaming data in envi-ronments with limited memory and computational resources. It delves into the application of data distillation as an innovative approach to improve the precision of streaming image data classification. By focusing on distilling essential features from data streams, our method aims to minimize computational demands while preserving crucial information for accurate classification. Our investigation com-pares this approach against traditional algorithms like Hoeffding Trees and Adap-tive Random Forest, adapted through embeddings for image data. The Distillation Based Classification (DBC) demonstrated superior performance, achieving a 73.1% accuracy rate, surpassing both traditional methods and Reservoir Sam-pling Based Classification (RBC) technique. This marks a significant advance-ment in streaming data classification, showcasing the effectiveness of our method in processing complex data streams and setting a new standard for accuracy and efficiency.
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