arXiv:2605.13278v2 Announce Type: replace-cross Abstract: Score-based diffusion models demonstrate superior performance in generative tasks but encounter fundamental bottlenecks in inverse problems due to the analytical intractability of the time-dependent likelihood score. To bridge this gap, we propose a novel proximal-based generative modeling (PGM) framework that rigorously circumvents explicit likelihood evaluation. Our framework is built upon a theoretical equivalence between Gaussian convolution in diffusion processes and Moreau-Yosida regularization in nonsmooth optimization. This enab
The paper addresses a fundamental bottleneck in applying highly performant score-based diffusion models to real-world inverse problems, a critical area for AI advancement.
This research provides a theoretical and practical framework to broaden the applicability of state-of-the-art generative AI to complex inverse problems, which are prevalent in scientific discovery and engineering.
The ability to rigorously circumvent explicit likelihood evaluation for score-based diffusion models opens new avenues for their use beyond pure generative tasks, enhancing their utility in problem-solving where inference from observed data is crucial.
- · AI researchers and deep learning practitioners
- · Sectors reliant on inverse problem solving (e.g., medical imaging, computational
- · Developers of generative AI frameworks
- · Traditional inverse problem solving methods that are less efficient or accurate
- · AI models less capable of handling analytically intractable likelihoods
Improved performance and broader application of generative AI in fields requiring complex inference from data.
Acceleration of research and development in scientific computing, material science, and personalized medicine through enhanced inverse problem capabilities.
Potential for new AI-driven product and service offerings that leverage advanced generative modeling for data-sparse or complex real-world challenges.
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