Smoother Flow Matching via Contrastive Trajectory Repulsion

arXiv:2610.01408 · cs.CV, cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/HKUST-LongGroup/CoFlow

Terminology

• Flow Matching — 13× in this paper · explained in 7 episodes
• Generative Model — 10× in this paper · explained in 2 episodes
• Generative Models — 10× in this paper · explained in 2 episodes
• Contrastive Learning — 7× in this paper · explained in 5 episodes
• Generative Modeling — 7× in this paper · explained in 3 episodes
• Distillation — 6× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Fidelity — 3× in this paper · explained in 2 episodes
• Score Function — 3× in this paper · explained in 4 episodes
• Stochastic Differential Equation — 3× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Classifier-Free Guidance — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Number of Function Evaluations — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Inception Score — 1× in this paper
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes

Sources

• Building Normalizing Flows with Stochastic Interpolants
• Stochastic interpolants with data-dependent couplings
• Diffusion Posterior Sampling for General Noisy Inverse Problems
• Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling
• Generative Modeling via Drifting
• One Step Diffusion via Shortcut Models
• Mean Flows for One-step Generative Modeling
• Classifier-Free Diffusion Guidance
• Flow Matching for Generative Modeling
• I$^2$SB: Image-to-Image Schr\"odinger Bridge
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Decoupled Weight Decay Regularization
• Generating Images with Sparse Representations
• Progressive Distillation for Fast Sampling of Diffusion Models
• Score-Based Generative Modeling through Stochastic Differential Equations
• Improving and generalizing flow-based generative models with minibatch optimal transport
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
• Denoising Diffusion Bridge Models

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