Align & Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment
arXiv:2511.16870v3 Announce Type: replace-cross Abstract: Enforcing alignment between the internal representations of diffusion or flow-based generative models and those of pretrained self-supervised encoders has recently been shown to provide a powerful inductive bias, improving both convergence and sample quality. In this work, we extend this idea to inverse problems, where pretrained generative models are employed as priors. We propose applying representation alignment (REPA) between diffusion or flow-based models and a DINOv2 visual encoder, to guide the reconstruction process at inference
The continuous advancements in generative AI, particularly diffusion and flow-based models, and self-supervised learning are enabling more sophisticated approaches to inverse problems and reconstruction.
Improving the ability of generative models to solve inverse problems could significantly enhance practical applications in various fields, from medical imaging to computer vision, by allowing more accurate and robust data reconstruction.
The proposed 'Align & Invert' method introduces a new, more effective paradigm for guiding generative models in inverse problem-solving through representation alignment, potentially leading to higher quality and faster solutions.
- · AI researchers and developers
- · Medical imaging sector
- · Computer vision applications
- · Generative AI model providers
- · Traditional inverse problem-solving methods
Improved performance and accuracy in tasks requiring reconstruction from partial or noisy data.
Accelerated development and deployment of AI systems in scientific discovery, healthcare diagnostics, and content creation.
Enhanced AI capabilities contribute to broader debates on AI agency and the increasing sophistication of machine intelligence in interpretation and creation.
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Read at arXiv cs.LG