Shared-Factor-Conditioned Diffusion for Low-Rank Tensor Recovery

The paper introduces DiffTR, an unsupervised framework that unifies tensor completion, denoising, and hyperspectral super‑resolution using coordinate‑conditioned diffusion. Shared factors indexed by tensor coordinates condition a compact score network, allowing the model to learn both the factors and an implicit tensor representation from observations. The framework separates representation learning from task‑specific observation modeling, enabling reverse diffusion for missing‑value recovery, Tweedie‑based denoising, and measurement‑guided sampling for high‑resolution reconstruction, all while supporting multiple tensor modalities and corruption models.

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• diffusion