A unified framework decomposes Wasserstein gradient flow velocity fields across f-divergences into a shared beta direction and divergence-specific weighting, enabling data-free one-step sampling.
hub
Sinkhorn-Drifting Generative Models.arXiv preprint arXiv:2603.12366, 2026a
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
hub tools
citation-role summary
background 3
citation-polarity summary
years
2026 12roles
background 3polarities
background 3representative citing papers
For companion-elliptic kernels vanishing drifting fields identify target measures exactly, and field convergence yields weak convergence once mass escape to infinity is detected by a single C0 scalar.
SROT regularizes the OT transport plan toward a sliced OT reference, yielding better approximations of exact OT than entropic OT and improving on the sliced OT plan itself.
A new constrained gradient flow on the space of transport maps converges to the OT map and enables more stable and accurate training of convexity-constrained neural networks for learning Monge maps.
DFP is a one-step generative policy using Wasserstein gradient flow on a drifting model backbone, with a top-K behavior cloning surrogate, that reaches SOTA on Robomimic and OGBench manipulation tasks.
SymDrift makes drifting models produce symmetry-invariant samples in one step via symmetrized coordinate drifts or G-invariant embeddings, outperforming prior one-shot baselines on molecular benchmarks and cutting compute by up to 40x.
The paper interprets GMD algorithms as limiting points of Wasserstein gradient flows on KL divergence with Parzen smoothing and on Sinkhorn divergence, while extending the approach to MMD, sliced Wasserstein, and GAN critics.
RA-OT and OA-OT amortize optimal transport by regressing or optimizing sliced-OT Kantorovich potentials to approximate full OT plans efficiently across multiple measure pairs.
Drift Flow Matching connects direct transport maps from Drift Models with flow-based iterative refinement to enable adaptive computation in generative modeling.
A simplified one-step diffusion distillation uses pretrained teacher features directly for drifting loss plus a mode coverage term, achieving FID 1.58 on ImageNet-64 and 18.4 on SDXL.
citing papers explorer
-
A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
A unified framework decomposes Wasserstein gradient flow velocity fields across f-divergences into a shared beta direction and divergence-specific weighting, enabling data-free one-step sampling.
-
Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families
For companion-elliptic kernels vanishing drifting fields identify target measures exactly, and field convergence yields weak convergence once mass escape to infinity is detected by a single C0 scalar.
-
Sliced-Regularized Optimal Transport
SROT regularizes the OT transport plan toward a sliced OT reference, yielding better approximations of exact OT than entropic OT and improving on the sliced OT plan itself.
-
Learning Monge maps with constrained drifting models
A new constrained gradient flow on the space of transport maps converges to the OT map and enables more stable and accurate training of convexity-constrained neural networks for learning Monge maps.
-
Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow
DFP is a one-step generative policy using Wasserstein gradient flow on a drifting model backbone, with a top-K behavior cloning surrogate, that reaches SOTA on Robomimic and OGBench manipulation tasks.
-
SymDrift: One-Shot Generative Modeling under Symmetries
SymDrift makes drifting models produce symmetry-invariant samples in one step via symmetrized coordinate drifts or G-invariant embeddings, outperforming prior one-shot baselines on molecular benchmarks and cutting compute by up to 40x.
-
On the Wasserstein Gradient Flow Interpretation of Drifting Models
The paper interprets GMD algorithms as limiting points of Wasserstein gradient flows on KL divergence with Parzen smoothing and on Sinkhorn divergence, while extending the approach to MMD, sliced Wasserstein, and GAN critics.
-
Amortized Optimal Transport from Sliced Potentials
RA-OT and OA-OT amortize optimal transport by regressing or optimizing sliced-OT Kantorovich potentials to approximate full OT plans efficiently across multiple measure pairs.
-
Drift Flow Matching
Drift Flow Matching connects direct transport maps from Drift Models with flow-based iterative refinement to enable adaptive computation in generative modeling.
-
Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations
A simplified one-step diffusion distillation uses pretrained teacher features directly for drifting loss plus a mode coverage term, achieving FID 1.58 on ImageNet-64 and 18.4 on SDXL.
- Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
- One-Step Generative Modeling via Wasserstein Gradient Flows