An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders

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An academic research report published on September 7, 2026, utilized a self-supervised encoder to perform cluster analysis on an unseen dataset. The study aimed to explore how self-supervised learning models can effectively extract features and group data in scenarios with no labels or new data.

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ImageNet-1kScott ClowearXiv

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A arXiv cs.AI en 2026-09-07 12:00

研究人员部署仅基于 ImageNet-1k 预训练的自监督编码器于未见过的数据集，利用常规聚类算法评估其嵌入是否形成有意义的簇。实验发现，在训练域内监督编码器效用更高，而在远外域（far-OOD）自监督编码器表现更优；但针对 ImageNet-1k 分类微调自监督编码器后，其在远外域的表现反而劣于仅使用自监督或仅使用监督的模型。聚类方法提供了一种独立于现有特征质量评估方法的自监督学习表示效用评估途径。此外，在 UMAP 降维空间下测量的轮廓系数与聚类性能高度相关，可用作无真实标签数据的聚类性能代理指标。相关代码实现已发布至 GitHub 仓库 zs-ssl-clustering/。