AI RESEARCH
A Unified View of Drifting and Score-Based Models
arXiv CS.LG
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ArXi:2603.07514v1 Announce Type: new Drifting models train one-step generators by optimizing a mean-shift discrepancy induced by a kernel between the data and model distributions, with Laplace kernels used by default in practice. At each point, this discrepancy compares the kernel-weighted displacement toward nearby data samples with the corresponding displacement toward nearby model samples, yielding a transport direction for generated samples.