COFM: Consistent Optimal Transport Flow Matching via Partially Input Convex Neural Networks

arXiv:2511.06042 · cs.LG · Submitted 2025-11-08 · Read on arXiv

cs.LG

Submitted: 2025-11-08

Updated: 2026-08-27

Code: https://github.com/Jhomanik/Optimal-Flow-Matching

Terminology

• Consistency — 24× in this paper · explained in 3 episodes
• Optimal Transport — 23× in this paper · explained in 5 episodes
• Flow Matching — 22× in this paper · explained in 7 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Normalizing Flow — 5× in this paper · explained in 3 episodes
• Normalizing Flows — 5× in this paper · explained in 4 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Latent Space — 3× in this paper · explained in 7 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Temporal consistency — 3× in this paper · explained in 2 episodes
• Wasserstein Distance — 3× in this paper · explained in 4 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• 2-Wasserstein Distance — 1× in this paper · explained in 1 episode
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency OT Flow Match — 1× in this paper
• Convexity Embedding Layer — 1× in this paper
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)

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