DiEC: Diffusion Embedded Clustering

• URL: http://arxiv.org/abs/2512.20905v2
• Date: Thu, 25 Dec 2025 16:22:07 GMT
• Title: DiEC: Diffusion Embedded Clustering
• Authors: Haidong Hu,
• Abstract summary: Deep clustering critically depends on representations that expose clear cluster structure.<n>Most prior methods learn a single embedding with an autoencoder or a self-supervised encoder and treat it as the primary representation for clustering.<n>We propose Embedded Diffusion Clustering (DiEC), an unsupervised clustering framework that exploits this trajectory by directly leveraging intermediate activations of a pretrained diffusion U-Net.
• Score: 0.76629754443761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep clustering critically depends on representations that expose clear cluster structure, yet most prior methods learn a single embedding with an autoencoder or a self-supervised encoder and treat it as the primary representation for clustering. In contrast, a pretrained diffusion model induces a rich representation trajectory over network layers and noise timesteps, along which clusterability varies substantially. We propose Diffusion Embedded Clustering (DiEC), an unsupervised clustering framework that exploits this trajectory by directly leveraging intermediate activations of a pretrained diffusion U-Net. DiEC formulates representation selection over layer * timestep and adopts a practical two-stage procedure: it uses the U-Net bottleneck as the Clustering Middle Layer (CML, l*) and identifies the Clustering-Optimal Timestep (COT, t*) via an efficient subset-based, noise-averaged search. Conditioning on (l*, t*), DiEC learns clustering embeddings through a lightweight residual mapping, optimized with a DEC-style KL self-training objective and structural regularization, while a parallel random-timestep denoising-consistency loss stabilizes training and preserves diffusion behavior. Experiments on standard benchmarks demonstrate that DiEC achieves strong clustering performance and reveal the importance of selecting diffusion representations for clustering.

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