Outlier-Robust Diffusion Solvers for Inverse Problems
Quick summary
arXiv:2605.09477v2 Announce Type: replace-cross Abstract: Methods based on diffusion models (DMs) for solving inverse problems (IPs) have recently achieved remarkable performance. However, DM-based methods typically struggle against outliers, which are common in real-world measurements. In this work, to tackle IPs with outliers, we first refine the measurement via explicit noise estimation to mitigate the effect of noise. Subsequently, we formulate an iteratively reweighted least squares objective based on the Huber loss to address the outliers. We propose a method utilizing gradient descent t
Key takeaways
- arXiv:2605.09477v2 Announce Type: replace-cross Abstract: Methods based on diffusion models (DMs) for solving inverse problems (IPs) have recently achieved remarkable performance.
- However, DM-based methods typically struggle against outliers, which are common in real-world measurements.
- In this work, to tackle IPs with outliers, we first refine the measurement via explicit noise estimation to mitigate the effect of noise.
Why it matters
The importance of “Outlier-Robust Diffusion Solvers for Inverse Problems” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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