Where Flow Matching Leaks: Characterising Membership Signals Along the Interpolation Path

arXiv:2606.07271 · cs.LG, cs.AI, cs.SD · Submitted 2026-06-05 · Read on arXiv

cs.LG, cs.AI, cs.SD

Submitted: 2026-06-05

Updated: 2026-08-11

Terminology

• Flow Matching — 49× in this paper · explained in 8 episodes
• Transformer — 18× in this paper · explained in 2 episodes
• Ablation — 17× in this paper · explained in 1 episode
• Diffusion Model — 14× in this paper · explained in 8 episodes
• Latent Space — 14× in this paper · explained in 7 episodes
• Diffusion Models — 13× in this paper · explained in 6 episodes
• Reconstruction Error — 10× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Latent Representations — 5× in this paper · explained in 3 episodes
• Trace — 5× in this paper · explained in 2 episodes
• KL Regularization — 4× in this paper · explained in 2 episodes
• Rectified Flow — 4× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• F REE M USIC A RCHIVE — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Higher-order statistics — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Stable Audio Open
• How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?
• Music2Latent: Consistency Autoencoders for Latent Audio Compression

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks