Unsupervised Concept Discovery with Dirichlet Concept Diffusion Models

We show that a generative model can discover concepts from data in an unsupervised setting while learning to generate. The Dirichlet Concept Diffusion Model (DCDM) embeds concept discovery into diffusion-based generation. DCDM learns concept centers and infers a Dirichlet distribution over them for each input. The resulting weighted center shapes the forward diffusion mean and reverse denoising process, so components are learned through the same evidence lower bound used to model the data. The method uses no class labels, attribute annotations, captions, pretrained text-to-image priors, or semantic concept supervision. Analysis shows how the formulation preserves concept-level information along diffusion paths and reduces denoising ambiguity, creating pressure for concept centers to capture stable modes. Experiments across diverse image domains show that the learned components form coherent prototypes, support interventions, and reflect recurring visual structure.