$\texttt{InfoHier}$：基于编码与嵌入的分层信息提取
$\texttt{InfoHier}$: Hierarchical Information Extraction via Encoding and Embedding
AI总结：
针对自监督学习表征缺乏层级性、层次聚类依赖刚性度量的问题，提出结合二者的InfoHier框架，联合学习鲁棒潜在表征与分层结构，提升聚类和表征学习效果。
摘要：
分析大规模数据集，尤其是涉及图像这类复杂高维数据的数据集，极具挑战性。虽然自监督学习（SSL）已被证明能从未标记数据中学习表征，但它通常聚焦于扁平的非分层结构，忽略了许多真实数据集中存在的多层级关系。层次聚类（HC）可通过将数据组织成树状结构来揭示这些关系，但它往往依赖刚性的相似性度量，难以捕捉多样数据类型的复杂性。为解决这些问题，我们提出了$\texttt{InfoHier}$——一个结合SSL与HC的框架，用于联合学习鲁棒的潜在表征与分层结构。该方法利用SSL提供自适应表征，增强HC捕捉复杂模式的能力；同时整合HC损失来优化SSL训练，使表征更契合底层的信息层级结构。$\texttt{InfoHier}$有望提升聚类与表征学习的表达能力和性能，为数据分析、管理和信息检索带来显著效益。
英文摘要：
Analyzing large-scale datasets, especially involving complex and high-dimensional data like images, is particularly challenging. While self-supervised learning (SSL) has proven effective for learning representations from unlabelled data, it typically focuses on flat, non-hierarchical structures, missing the multi-level relationships present in many real-world datasets. Hierarchical clustering (HC) can uncover these relationships by organizing data into a tree-like structure, but it often relies on rigid similarity metrics that struggle to capture the complexity of diverse data types. To address these we envision $\texttt{InfoHier}$, a framework that combines SSL with HC to jointly learn robust latent representations and hierarchical structures. This approach leverages SSL to provide adaptive representations, enhancing HC's ability to capture complex patterns. Simultaneously, it integrates HC loss to refine SSL training, resulting in representations that are more attuned to the underlying information hierarchy. $\texttt{InfoHier}$ has the potential to improve the expressiveness and performance of both clustering and representation learning, offering significant benefits for data analysis, management, and information retrieval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
