Globscope：迈向损失景观的全局视图
Globscope: Toward a Global View of the Loss Landscape
为视觉研究者提供一种在低维非线性流形上全局可视化并重建完整模型权重的手段，可直接用于分析模型合并、模式连通性与置换对称性（re-basin），帮助判断不同视觉模型解之间的连通路径与低损失通道。
原文依据 Globscope learns a low-dimensional non-linear manifold of model parameters using an autoencoder
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论理解神经网络损失景观的全局结构，对于深入认识模型合并、超参数选择、泛化能力以及不同解之间的关系具有重要意义。由于神经网络参数空间维度极高，可视化损失景观的全局结构极具挑战性。已有工作主要聚焦于单个盆地周围的损失景观可视化，忽略了不同极小值或不同解之间如何相互关联。我们提出 Globscope，一个能够在多个解或盆地之间提供损失景观全局视图的框架。Globscope 利用自编码器学习模型参数的低维非线性流形，从而同时支持潜空间可视化与完整模型权重的重建。随后，它通过拓扑数据分析在该流形上总结各极小值之间的关系与连接。我们的框架能够生成连续、可解释的可视化结果，揭示损失景观中的全局连通模式。我们将 Globscope 与基于核的方法进行比较，并证明其在跨多样化解保持全局结构方面具有更优性能。我们进一步展示了 Globscope 可用于分析两个应用：利用模式连通性算法揭示不同解之间的全局低损失解路径，以及利用 re-basin 方法可视化不同解的置换对称性。
英文原文摘要 1475 字符
Understanding the global structure of neural network loss landscapes is important for gaining insight into model merging, hyperparameter selection, generalization, and the relationships between distinct solutions. Visualizing the global structure of loss landscapes is very challenging because of the high dimensionality of the parameter space of neural networks. Prior work has primarily focused on visualizing the loss landscape around one single basin, missing how different minima or solutions relate to each other. We introduce Globscope, a framework that provides a global view of the loss landscape across multiple solutions or basins. Globscope learns a low-dimensional non-linear manifold of model parameters using an autoencoder, enabling both latent-space visualization and reconstruction of full model weights. Then it summarizes the relations and connections among minima on this manifold through topological data analysis. Our framework produces continuous, interpretable visualizations that reveal global connectivity patterns in the landscape. We compare Globscope with kernel-based methods and demonstrate its superior performance in preserving the global structure across diverse solutions. We further show how Globscope can be used to analyze two applications: revealing global low-loss solution pathways between distinct solutions using mode connectivity algorithms, and visualizing permutation symmetries of different solutions using re-basin approaches.