On the spectral properties of generative denoiser Jacobians

arXiv:2609.36210 · cs.LG, cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Regularization — 95× in this paper · explained in 3 episodes
• Generative Model — 14× in this paper · explained in 2 episodes
• Generative Models — 11× in this paper · explained in 2 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Latent Space — 5× in this paper · explained in 7 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• International Conference on Computer Vision — 2× in this paper
• Embeddings — 1× in this paper · explained in 2 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• On the Importance of Noise Scheduling for Diffusion Models
• Classifier-Free Diffusion Guidance
• Input Perturbation Reduces Exposure Bias in Diffusion Models
• Diffuse and Disperse: Image Generation with Representation Regularization

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