FP-FM adapts flow matching models to unseen distributions via least-squares projection onto basis functions spanning training velocity fields, yielding improved precision and recall without inference-time training.
High- resolution image synthesis with latent diffusion models
2 Pith papers cite this work. Polarity classification is still indexing.
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AHPA adaptively aligns diffusion transformers to hierarchical VAE priors via a dynamic router that matches supervision granularity to the current noise level, improving convergence and quality.
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A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
FP-FM adapts flow matching models to unseen distributions via least-squares projection onto basis functions spanning training velocity fields, yielding improved precision and recall without inference-time training.
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AHPA: Adaptive Hierarchical Prior Alignment for Diffusion Transformers
AHPA adaptively aligns diffusion transformers to hierarchical VAE priors via a dynamic router that matches supervision granularity to the current noise level, improving convergence and quality.