无需真实数据，从噪声观测中自监督学习信号恢复方法。
Self-Supervised Learning from Noisy and Incomplete Data
- 利用观测数据自身构建监督信号，避免依赖真实标签
- 理论分析支持多种逆问题的自监督求解可行性
- 适用于医学成像等缺乏真实参考的场景
科学与工程中的许多重要问题需要从噪声和/或不完整观测中推断信号，且观测过程已知。传统方法常采用手工设计的正则化（如稀疏性、总变差）获得合理估计。近年来的数据驱动方法通过从真实信号与对应观测的示例中直接学习求解器，提供更优方案。然而在许多实际应用中，获取训练所需的真实参考数据成本高昂甚至不可能。自监督学习方法提供了一种有前景的替代路径，仅使用测量数据即可学习求解器，无需真实参考。本文全面总结了用于反问题的各类自监督方法，特别关注其理论基础，并展示了在成像反问题中的实际应用。
原文摘要 · Abstract (English)
Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this problem has been tackled using hand-crafted regularization (e.g., sparsity, total-variation) to obtain meaningful estimates. Recent data-driven methods often offer better solutions by directly learning a solver from examples of ground-truth signals and associated observations. However, in many real-world applications, obtaining ground-truth references for training is expensive or impossible. Self-supervised learning methods offer a promising alternative by learning a solver from measurement data alone, bypassing the need for ground-truth references. This manuscript provides a comprehensive summary of different self-supervised methods for inverse problems, with a special emphasis on their theoretical underpinnings, and presents practical applications in imaging inverse problems.
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