arXiv Machine Learning
Follow the Mean: Reference-Guided Flow Matching
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arXiv Machine Learning
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Gradual Fine-Tuning for Flow Matching Models
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arXiv Machine Learning
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Difficulty-Calibrated Interpolation Paths for Conditional Flow Matching
The paper introduces Difficulty-Calibrated Flow Matching, a method that adapts the noise-to-data interpolation schedule in Conditional Flow Matching based on a pilot run’s loss profile. By setting the schedule to the quantile function of this difficulty profile, the training trajectory spends more time where the velocity is hardest to learn. Experiments on CIFAR-10, MNIST, and Fashion‑MNIST show that this calibrated path achieves the best FID on CIFAR‑10 and outperforms all fixed schedules in large‑batch, few‑update settings, where compute is most limited.
arXiv Machine Learning
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Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows
arXiv:2607. 14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining.