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Drifting Models: one-pass generators trained with sample drift

Contribution grade

Generative-model training · Human-designed research method or resource

Reference score75,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Trained a one-pass pushforward generator toward stopped-gradient targets constructed from attraction/repulsion drift fields between generated and data samples, moving iterative distribution updates into training.

Scientific scope

Trained a one-pass pushforward generator toward stopped-gradient targets constructed from attraction/repulsion drift fields between generated and data samples, moving iterative distribution updates into training. Inherits mean-shift, kernels and moment/contrastive ideas. Finite image experiments are not universal convergence or first-ever one-step generation. Zero-drift converse depends on the field. Later analysis distinguishes smoothed score difference from true smoothed-KL gradient (extra convolution), and its modified Sinkhorn proxy from original joint-normalization/self-exclusion implementation. No blanket Wasserstein guarantee.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits mean-shift, kernels and moment/contrastive ideas. Finite image experiments are not universal convergence or first-ever one-step generation. Zero-drift converse depends on the field. Later analysis distinguishes smoothed score difference from true smoothed-KL gradient (extra convolution), and its modified Sinkhorn proxy from original joint-normalization/self-exclusion implementation. No blanket Wasserstein guarantee.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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