Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models

arXiv:2608.30081 · cs.LG, cs.CV · Submitted 2026-08-30 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-08-30

Updated: 2026-09-02

Terminology

• Latent Space — 12× in this paper · explained in 7 episodes
• Flow Matching — 11× in this paper · explained in 8 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Latent Representation — 6× in this paper · explained in 2 episodes
• Semantic Similarity — 6× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• In International Conference on Machine Learning — 3× in this paper
• Influence Functions — 3× in this paper · explained in 2 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Flow Matching Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 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
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?

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