StreamFP：用于高效流学习的可学习指纹引导数据选择
StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning
AI总结：
针对流学习中动态数据选择效率不足的问题，提出StreamFP，利用可学习指纹引导核心集选择与缓冲区更新，在多种数据到达速率下显著提升准确率和训练吞吐量。
摘要：
流学习（SL）要求模型能够快速适应持续演化的数据，这在计算效率和学习准确性两方面都带来了重大挑战。有效的数据选择在SL中至关重要，可确保信息保留与训练效率之间的平衡。传统基于规则的数据选择方法难以适应流数据的动态特性，这凸显了提出创新解决方案以有效应对这些挑战的必要性。近期用于处理不断变化的数据分布的方法面临诸多限制，使其在快节奏环境中的有效性受限。为此，我们提出StreamFP，这是一种新颖方法，独特地采用称为指纹的动态、可学习参数，以提升流学习中数据选择的效率和适应性。StreamFP通过其独特的指纹引导机制优化核心集选择，以实现高效训练，同时确保稳健的缓冲区更新，自适应地响应数据动态，这使其有别于现有流学习方法。实验结果表明，在不同数据到达速率下，StreamFP相较基线模型分别实现15.99%、29.65%和51.24%的准确率提升，优于最先进方法，同时训练吞吐量提高4.6倍。
英文摘要：
Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based data selection methods struggle to accommodate the dynamic nature of streaming data, highlighting the necessity for innovative solutions that effectively address these challenges. Recent approaches to handling changing data distributions face challenges that limit their effectiveness in fast-paced environments. In response, we propose StreamFP, a novel approach that uniquely employs dynamic, learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its unique fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that adaptively respond to data dynamics, setting it apart from existing methods in stream learning. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
