The paper investigates whether pretrained image models can generalize to unseen datasets by clustering their embeddings. Using encoders trained only on ImageNet‑1k, both supervised and self‑supervised, the authors evaluate clustering performance on out‑of‑domain images. They find that supervised encoders perform better within the training domain, while self‑supervised encoders excel far outside it, and that fine‑tuning self‑supervised models reverses this trend. Additionally, the study shows that the silhouette score in UMAP‑reduced space correlates strongly with clustering accuracy, offering a proxy metric when labels are unavailable.
By Scott C. Lowe, Joakim Bruslund Haurum, Sageev Oore, Thomas B. Moeslund, Graham W. Taylor
arXiv:2609.36648v1 Announce Type: new
Abstract: Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics...
By Yuanwei Hu, Bo Peng, Yuheng Jia, Xinting Hu, Yadan Luo, Wenjie Zhu
The paper examines whether Deep Embedded Clustering (DEC) truly overcomes the fundamental limitations of k‑means clustering, such as handling clusters of arbitrary shapes, varied sizes, and densities. Through analysis, it finds that DEC does not exploit the underlying data distribution and therefore fails to address these limitations. Instead, a non‑deep learning approach that leverages distributional information of clusters can achieve the intended goals of deep clustering.
By Kai Ming Ting, Wei-Jie Xu, Hang Zhang
arXiv:2608. 00346v1 Announce Type: new Abstract: Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy.
By Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan, Manar D. Samad
arXiv:2509. 25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue.
By Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim, Guillaume Rabusseau, Vladimir Makarenkov
arXiv:2403.14830v2 Announce Type: replace
Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensio...
By Zeya Wang, Chenglong Ye
arXiv:2606. 18833v1 Announce Type: new Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection.
By Nassir Mohammad
arXiv:2603. 15553v2 Announce Type: replace-cross Abstract: The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.
By Scott C. Lowe, Anthony Fuller, Sageev Oore, Evan Shelhamer, Graham W. Taylor
arXiv:2607. 06151v1 Announce Type: new Abstract: Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data.
By Yao Fu, Chunxia Zhang, Junmin Liu, Yihang Jin, Haishan Ye, Yuanao Yang
The paper introduces ClusterMixer, a token mixer based on hierarchical clustering in hyperbolic space, designed to be transparent and interpretable. It forms the core of a new vision backbone called HCFormer, which incorporates multiple clustering strategies to maintain strong performance. Experiments show HCFormer surpasses existing backbones on tasks such as image classification, object detection, instance segmentation, and semantic segmentation.
By Jianan Wei, Guikun Chen, Zhiyuan Weng, Chunchao Guo, Yujia Wang, Wenguan Wang
The paper introduces FedDCN, a federated deep clustering network that jointly optimizes reconstruction and clustering losses for high‑dimensional, heterogeneous data. It addresses challenges of non‑IID client data by generating synthetic augmentations and applying geometric regularization to align latent spaces. Experiments show the method’s effectiveness under both IID and non‑IID settings, and the authors outline future research directions.
By Morris Stallmann, Charalampos S. Kouzinopoulos, Marcin Pietrasik, Anna Wilbik
The paper introduces a selective inference framework tailored for deep clustering that uses a fixed pretrained encoder to map high‑dimensional data into a latent space before clustering. It addresses the complex selection bias arising from the nonlinear transformation and offers a computationally tractable method to perform valid statistical tests on cluster differences. Experiments on synthetic data show controlled Type I error and higher power compared to conservative baselines, while genomic case studies demonstrate the ability to uncover significant cluster differences while properly accounting for selection bias.
By Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino, Ichiro Takeuchi