DEFAR

DEFAR framework uses exposure bias signals to self-rectify Flow Matching during training

A new arXiv preprint introduces DEFAR (DirEctional-Frequency Adaptive Rectification), a training framework for Flow Matching generative models that addresses exposure bias — the train/inference discrepancy — by extracting dynamic correction signals from the bias itself. The method has two components: Anti-Drift Rectification (ADR), which steers deviated inference states back toward targets, and Frequency Compensation (FC), which reinforces missing low-frequency components using bias as a self-feedback weight. Experiments on CIFAR-10, CelebA-64, and ImageNet-256/512 show improvements over prior baselines with favorable scalability and inference robustness.