  Cross-modality data translation has attracted great interest in image
computing. Deep generative models (\textit{e.g.}, GANs) show performance
improvement in tackling those problems. Nevertheless, as a fundamental
challenge in image translation, the problem of Zero-shot-Learning
Cross-Modality Data Translation with fidelity remains unanswered. This paper
proposes a new unsupervised zero-shot-learning method named Mutual Information
guided Diffusion cross-modality data translation Model (MIDiffusion), which
learns to translate the unseen source data to the target domain. The
MIDiffusion leverages a score-matching-based generative model, which learns the
prior knowledge in the target domain. We propose a differentiable
local-wise-MI-Layer ($LMI$) for conditioning the iterative denoising sampling.
The $LMI$ captures the identical cross-modality features in the statistical
domain for the diffusion guidance; thus, our method does not require retraining
when the source domain is changed, as it does not rely on any direct mapping
between the source and target domains. This advantage is critical for applying
cross-modality data translation methods in practice, as a reasonable amount of
source domain dataset is not always available for supervised training. We
empirically show the advanced performance of MIDiffusion in comparison with an
influential group of generative models, including adversarial-based and other
score-matching-based models.
