Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion

• URL: http://arxiv.org/abs/2301.13743v1
• Date: Tue, 31 Jan 2023 16:24:34 GMT
• Title: Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion
• Authors: Zihao Wang, Yingyu Yang, Maxime Sermesant, Herv\'e Delingette, Ona Wu
• Abstract summary: Cross-modality data translation has attracted great interest in image computing. This paper proposes a new unsupervised zero-shot-learning method named Mutual Information Diffusion guided cross-modality data translation Model (MIDiffusion) We empirically show the advanced performance of MIDiffusion in comparison with an influential group of generative models.
• Score: 5.795193288204816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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