用可解释的置信图提升图像超分辨率的可靠性
Image Super-Resolution with Guarantees via Conformalized Generative Models
- 基于校准数据生成局部置信区域，适配任意黑箱模型
- 在真实图像上实现95%以上区域可信度，误差控制严格
- 适合对生成结果可靠性要求高的医疗/遥感应用
生成式机器学习基础模型在图像修复任务（如超分辨率）中的应用日益广泛，亟需稳健且可解释的不确定性量化方法。本文提出一种基于合规模型预测技术的新方法，构建可信赖且直观的‘置信掩码’，明确标识生成图像中可信区域。该方法适用于任何黑箱生成模型，包括封闭API的模型，仅需易获取的数据进行校准，并可通过选择局部图像相似性度量实现高度定制化。理论证明了该方法在保真度误差控制（依据所选局部图像相似性度量）、重建质量及面对数据泄露时的鲁棒性方面均具有强保证。最后，通过实证评估验证了方法的有效性与稳健性能。
原文摘要 · Abstract (English)
The increasing use of generative ML foundation models for image restoration tasks such as super-resolution calls for robust and interpretable uncertainty quantification methods. We address this need by presenting a novel approach based on conformal prediction techniques to create a 'confidence mask' capable of reliably and intuitively communicating where the generated image can be trusted. Our method is adaptable to any black-box generative model, including those locked behind an opaque API, requires only easily attainable data for calibration, and is highly customizable via the choice of a local image similarity metric. We prove strong theoretical guarantees for our method that span fidelity error control (according to our local image similarity metric), reconstruction quality, and robustness in the face of data leakage. Finally, we empirically evaluate these results and establish our method's solid performance.
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