RepFlow: Reciprocal Supervision Improves Generation and Representation in Flow Models

arXiv:2609.33217 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Representation Learning — 14× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Accuracy — 10× in this paper · explained in 1 episode
• Linear Probing — 9× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Distillation — 6× in this paper · explained in 2 episodes
• Distribution Matching — 6× in this paper · explained in 2 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Generative Modeling — 6× in this paper · explained in 3 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Consistency Distillation — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative AI — 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
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Label-Efficient Semantic Segmentation with Diffusion Models
• Flow map matching with stochastic interpolants: A mathematical framework for consistency models
• Generative Modeling via Drifting
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Image Generators are Generalist Vision Learners
• No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
• Flow Matching for Generative Modeling
• One-step Latent-free Image Generation with Pixel Mean Flows
• DINOv2: Learning Robust Visual Features without Supervision
• Progressive Distillation for Fast Sampling of Diffusion Models
• Representation Alignment for Just Image Transformers is not Easier than You Think
• What matters for Representation Alignment: Global Information or Spatial Structure?
• Is Noise Conditioning Necessary for Denoising Generative Models?
• Diffuse and Disperse: Image Generation with Representation Regularization
• Representation Fr'echet Loss for Visual Generation
• Flow Matching in the Low-Noise Regime: Pathologies and a Contrastive Remedy

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