arXiv Machine Learning
Aug 26

How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning

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 Machine Learning
Jul 9

Converge to Surprise: Evolutionary Self-supervised Image Clustering

arXiv:2607. 06887v1 Announce Type: new Abstract: Most self-supervised image clustering models, actually almost all deep learning approaches, are based on gradient descent: In order to calculate the loss, every optimization step requires a clearly defined target, whether a contrastive split, a masked patch or entity, an EMA-teacher output, a pseudo-label, or a differentiable information-theoretic functional.

By Canlin Zhang, Xiuwen Liu