ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold. Due to the success of diffusion models and flow matching, one of the more common beliefs is the importance of transforming the noise distribution to the data distribution gradually through many small transformations. We ask whether this is truly necessary, and take a minimalist approach to designing a competitive generative model. We sta
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- PossiblePossibly related (embedding) · 47%Diffusion →
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “ROMS-IMLE: A Minimalist Approach to Competitive Single-Step ” ≈ “GoogleCloudPlatform/generative-ai””
- LinkedLinked via arxiv author · 85%Chirag Vashist →
“ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling”
- LinkedLinked via arxiv author · 85%Like Liu →
“ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling”
- PossiblePossibly related (embedding) · 54%Diffusion Models: The Deep Learning Architecture Behind Modern Generative AI - Snowflake →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”