Unverified50% confidenceFactExact time
This research proposes two iterative inference algorithms: Push-forward corrects a pretrained sampler by iteratively estimating the density ratio and moving samples along the displacement field, without retraining; Pull-back pulls the data toward the base distribution and fits the transformation model layer by layer.
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10/13/2026
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UnverifiedInference-time guidance steers sampling trajectories toward regions consistent with observations by adding the gradient of the observation likelihood at each step of reverse diffusion or flow, without requiring model retraining.73% similarVerifiedFine-tuning continues to update parameters on top of pre-trained weights through the backpropagation algorithm, adapting the model to a specific domain or task style.72% similarVerifiedTransfer learning refers to migrating model parameters pre-trained on large-scale datasets to a specific small-sample task for fine-tuning.71% similarUnverifiedCompared to score-function-based diffusion models, the flow matching training objective is more concise, and the trajectory can be designed as a rectified flow, enabling high-quality generation in fewer inference steps.71% similar
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