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EquiReg: Equivariance Regularized Diffusion for Inverse Problems
arXiv – CS AI|Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi, Abbas Mammadov, Jiachen Yao, Chuwei Wang, Anima Anandkumar||3 views
🤖AI Summary
Researchers propose EquiReg, a new framework that improves diffusion models for inverse problems like image restoration by keeping sampling trajectories on the data manifold. The method uses equivariance regularization to guide sampling toward symmetry-preserving regions, enabling high-quality reconstructions with fewer sampling steps.
Key Takeaways
- →EquiReg is a plug-and-play framework that improves posterior sampling in diffusion models by penalizing off-manifold trajectories.
- →The method leverages equivariant functions that naturally emerge from data augmentation and symmetries in training data.
- →EquiReg shows particular effectiveness under reduced sampling steps where other methods suffer quality degradation.
- →The framework demonstrates consistent improvements across linear and nonlinear image restoration tasks and PDE solving.
- →The approach addresses limitations of isotropic Gaussian approximations that can produce inconsistent reconstructions.
#diffusion-models#inverse-problems#image-restoration#machine-learning#equivariance#manifold-learning#computer-vision#arxiv
Read Original →via arXiv – CS AI
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