Föllmer processes are variationally optimal among generative diffusions because they minimize the impact of drift estimation error on path-space KL divergence, rendering different interpolation schedules statistically equivalent.
Denoising diffusion probabilistic models
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A Jacobi diffusion on graphon space is discretized into a graph-level generative process that matches the continuous process's first moment exactly and second moment up to a closed-form gap, enabling out-of-scale graph generation.
IPAD-CLIP adapts CLIP via artifact-aware text embeddings to detect multi-class local perceptual artifacts, backed by a new dataset of 3520 images with pixel-level masks.
citing papers explorer
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Variational Optimality of F\"ollmer Processes in Generative Diffusions
Föllmer processes are variationally optimal among generative diffusions because they minimize the impact of drift estimation error on path-space KL divergence, rendering different interpolation schedules statistically equivalent.
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DiPhon: Diffusion on Graphons for Scalable Graph Generation
A Jacobi diffusion on graphon space is discretized into a graph-level generative process that matches the continuous process's first moment exactly and second moment up to a closed-form gap, enabling out-of-scale graph generation.
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IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts
IPAD-CLIP adapts CLIP via artifact-aware text embeddings to detect multi-class local perceptual artifacts, backed by a new dataset of 3520 images with pixel-level masks.