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Uncertainty Visualization via Low-Dimensional Posterior Projections
Omer Yair, Elias Nehme, Tomer Michaeli
In ill-posed inverse problems, it is commonly desirable to obtain insight into the full spectrum of plausible solutions, rather than extracting only a single reconstruction. Inform…
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
We study the behavior of deterministic methods for solving inverse problems in imaging. These methods are commonly designed to achieve two goals: (1) attaining high perceptual qual…
From Posterior Sampling to Meaningful Diversity in Image Restoration
Noa Cohen, Hila Manor, Yuval Bahat +1
Image restoration problems are typically ill-posed in the sense that each degraded image can be restored in infinitely many valid ways. To accommodate this, many works generate a d…
Uncertainty Quantification via Neural Posterior Principal Components
Elias Nehme, Omer Yair, Tomer Michaeli
Uncertainty quantification is crucial for the deployment of image restoration models in safety-critical domains, like autonomous driving and biological imaging. To date, methods fo…
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification
Hila Manor, Tomer Michaeli
Denoisers play a central role in many applications, from noise suppression in low-grade imaging sensors, to empowering score-based generative models. The latter category of methods…
The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks
Mor Shpigel Nacson, Rotem Mulayoff, Greg Ongie +2
We study the type of solutions to which stochastic gradient descent converges when used to train a single hidden-layer multivariate ReLU network with the quadratic loss. Our result…