用物理启发的数据增强提升弱监督学习效率，减少对大量数据的依赖。
Improving the performance of weak supervision searches using data augmentation
- 采用粒子动量抖动和喷注旋转等物理启发方法增强数据
- 在数据量减少的情况下仍保持模型性能，显著提升训练效率
- 适合需要少样本高效训练的高能物理与科学计算场景
弱监督学习结合了真实数据训练的优势与信号特性利用能力，但通常需要大量信号数据，严重限制了其实际应用。本文提出通过数据增强来缓解这一问题，提升训练数据的数量与多样性。具体而言，我们采用基于物理的增强方法，如 $p_{\text{T}}$ smearing 和 jet rotation。实验表明，数据增强能显著提升弱监督学习的性能，使神经网络在大幅减少数据量的情况下仍能高效学习。
原文摘要 · Abstract (English)
Weak supervision combines the advantages of training on real data with the ability to exploit signal properties. However, training a neural network using weak supervision often requires an excessive amount of signal data, which severely limits its practical applicability. In this study, we propose addressing this limitation through data augmentation, increasing the training data's size and diversity. Specifically, we focus on physics-inspired data augmentation methods, such as $p_{\text{T}}$ smearing and jet rotation. Our results demonstrate that data augmentation can significantly enhance the performance of weak supervision, enabling neural networks to learn efficiently from substantially less data.
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。