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NeurIPS 2026INRIASorbonne Université & GoogleDiffusion models

Understanding diffusion models requires rethinking (again) generalization

Understanding diffusion models requires new theory since memorization and generalization are incompatible, so research should study what models learn before memorizing.

Pierre Marion, Yu-Han Wu

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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lenient 3/5
medium 4/10
strict 0/5
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NeurIPS 2026Google DeepMindGoogle DeepmindDeepMindGoogle Deep MindGoogleGANs & VAEs

MIND: Monge Inception Distance for Generative Models Evaluation

MIND uses sliced Wasserstein distance via sorting to evaluate generative models with 10x better sample efficiency, 100x faster computation, and greater adversarial robustness than FID.

Quentin Berthet, Clement CREPY, Romuald Elie, Klaus Greff and 2 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5