DiEC: Diffusion Embedded Clustering

Abstract: Deep clustering hinges on learning representations that are inherently clusterable. However, using a single encoder to produce a fixed embedding ignores the representation trajectory formed by a pretrained diffusion model across network hierarchies and noise timesteps, where clusterability varies substantially. We propose DiEC (Diffusion Embedded Clustering), which performs unsupervised clustering by directly reading internal activations from a pretrained diffusion U-Net. DiEC formulates representation selection as a two-dimensional search over layer x timestep, and exploits a weak-coupling property to decompose it into two stages. Specifically, we first fix the U-Net bottleneck layer as the Clustering-friendly Middle Layer (CML), and then use Optimal Timestep Search (OTS) to identify the clustering-optimal timestep (t*). During training, we extract bottleneck features at the fixed t* and obtain clustering representations via a lightweight residual mapping. We optimize a DEC-style KL self-training objective, augmented with adaptive graph regularization and entropy regularization to strengthen cluster structures. In parallel, we introduce a denoising-consistency branch at random timesteps to stabilize the representations and preserve generative consistency. Experiments show that DiEC achieves competitive clustering performance on multiple standard benchmarks.