TopoAlign: Topology-Aware Visual Representation Alignment
Neural networks encode inputs as high-dimensional vectors, known as representations, that capture how models process data by encoding task-relevant structure and semantics. Representation alignment refers to the degree to which different models, layers, or training conditions produce similar representations for the same inputs, with important implications for model interpretation, selection, and robustness analysis. Existing approaches to measure alignment primarily rely on geometric properties, such as neighborhood and cluster similarity, offering limited insight into the global organization of representations. In this work, we present TopoAlign, a topology-aware framework for visually c...
| Authors | Xinyuan Yan, Rita Sevastjanova, Mennatallah El-Assady et al. |
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| Primary category | cs.CG |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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