arXiv：cs.LG· Akash Kannan, Kiho Park, Victor Veitch·· 4 小时前AI 评分28
Anchor Divergence：对比学习中的语义几何新方法
Anchor Divergence for Semantic Geometry in Contrastive Learning
AI 导读
研究者提出 Anchor Divergences，通过对锚点分布建模，在固定的对比学习表征上指定上下文相关的语义几何，建立了锚点概率分布与表征空间上 Bregman 几何之间的对应关系。在检索任务上的实验表明，该方法能高效指定特定上下文的语义相似度，代码已开源。
正文
Abstract:This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity, which provides a single fixed geometry. Semantic similarity, however, is inherently context dependent: two images may be similar because they depict the same object, share a visual style, or are relevant to the same clinical finding. We show that contrastive representations naturally encompass a family of geometries that can be specialized to particular semantic structure. The key idea is to use an interplay between contrastive learning, exponential families, and information geometry to establish a correspondence between probability distributions over "anchors" and Bregman geometries on the representation space. We use this correspondence to define "Anchor Divergences", a method for specifying context-specific semantic geometries on fixed representations. Under this correspondence, modeling the anchor distribution models the geometry itself. Experiments on retrieval show that anchor divergences provide an effective and efficient way to specify context-specific semantic similarity.
| Comments: | Code is available at this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.06919 [cs.AI] |
| (or arXiv:2610.06919v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.06919 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Akash Kannan [view email]
[v1]
Fri, 2 Oct 2026 21:23:21 UTC (7,928 KB)
来源：arXiv：cs.LG · arxiv.org