ANaLOG: Anisotropic Native-Latent Operator Guidance for Solving Inverse Problems

arXiv:2609.31933 · eess.IV, cs.CV · Submitted 2026-09-25 · Read on arXiv

eess.IV, cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/LituRout/PSLD

Terminology

• Inverse Problem — 31× in this paper · explained in 2 episodes
• Diffusion Model — 18× in this paper · explained in 8 episodes
• Decode — 16× in this paper · explained in 2 episodes
• Diffusion Models — 16× in this paper · explained in 6 episodes
• Latent Space — 14× in this paper · explained in 7 episodes
• Calibration — 13× in this paper · explained in 7 episodes
• Latent Diffusion Model — 7× in this paper · explained in 2 episodes
• Latent Diffusion Models — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Regularization — 5× in this paper · explained in 3 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Two-stage training — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 2× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Covariance Propagation — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models (LDMs) — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Tweedie Moment Projected Diffusions For Inverse Problems
• Diffusion Posterior Sampling for General Noisy Inverse Problems
• A Survey on Diffusion Models for Inverse Problems
• Classifier-Free Diffusion Guidance
• Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
• Generative Modelling With Inverse Heat Dissipation
• Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs
• On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
• Improving the Diffusability of Autoencoders
• Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
• Score-Based Generative Modeling through Stochastic Differential Equations
• Wasserstein Auto-Encoders

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