提出实时数据感知的训练质量监控框架，可识别训练瓶颈并提升模型可靠性。
Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
- 基于数据使用效率与优化动态构建实时监控指标
- 能发现损失函数在次优区域停滞等传统方法忽略的问题
- 适合高风险场景下对深度学习训练过程进行可靠性保障
深度学习模型通过多层线性与非线性变换捕捉复杂表征，但其黑箱特性及多模态训练环境引发可靠性、鲁棒性与安全性问题，尤其在高风险应用中尤为突出。为此，本文提出YES训练边界（YES training bounds），一种实时、数据感知的神经网络训练认证与监控框架。该框架评估数据利用效率与优化动态，可有效判断训练进展并检测异常行为。实验表明，YES边界超越传统局部优化视角，能识别训练损失在次优区域停滞的情况。在合成数据与真实数据（如图像去噪任务）上验证，该框架有效实现训练质量认证，并指导调整以提升模型性能。通过集成至彩色云监控系统，提供实时评估能力，为深度学习训练质量保证树立新标准。
原文摘要 · Abstract (English)
Deep learning models excel at capturing complex representations through sequential layers of linear and non-linear transformations, yet their inherent black-box nature and multi-modal training landscape raise critical concerns about reliability, robustness, and safety, particularly in high-stakes applications. To address these challenges, we introduce YES training bounds, a novel framework for real-time, data-aware certification and monitoring of neural network training. The YES bounds evaluate the efficiency of data utilization and optimization dynamics, providing an effective tool for assessing progress and detecting suboptimal behavior during training. Our experiments show that the YES bounds offer insights beyond conventional local optimization perspectives, such as identifying when training losses plateau in suboptimal regions. Validated on both synthetic and real data, including image denoising tasks, the bounds prove effective in certifying training quality and guiding adjustments to enhance model performance. By integrating these bounds into a color-coded cloud-based monitoring system, we offer a powerful tool for real-time evaluation, setting a new standard for training quality assurance in deep learning.
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