用可视化分析损失曲面，帮研究人员看清模型训练背后的深层机制
LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
- 通过多尺度可视化整合全局与局部损失信息
- 揭示残差连接对ResNet-20的影响路径
- 适合模型诊断与可解释性研究者使用
现代机器学习依赖优化神经网络参数以学习复杂特征。除了训练过程外，分析损失函数相对于网络参数的结构（即损失曲面）可揭示架构与学习过程的深层信息。尽管已有方法可刻画单个解附近的局部损失结构，但包含多个局部极小值的全局损失结构仍难以直观理解与可视化。为此，我们提出LossLens——一个支持多尺度探索损失曲面的可视化分析框架。该框架将全局与局部度量整合为综合视图，增强模型诊断能力。我们在两个案例中验证其有效性：一是可视化残差连接对ResNet-20的影响；二是分析物理参数如何影响求解简单对流问题的物理信息神经网络（PINN）。
原文摘要 · Abstract (English)
Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. We demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.
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