Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching

Flow Matching (FM) has achieved remarkable generativeperformance, yet it suffers from exposure bias due to discrepancies be-tween training and inference. Existing mitigation strategies typicallyrely on static constraints or external heuristics. In this work, we pro-pose that exposure bias itself inherently contains dynamic signals thatcan guide its own rectification. To leverage this, we introduce DEFAR(DirEctional-Frequency Adaptive Rectification). This framework simu-lates the single-step inference process during training to identify exposurebias. It utilizes the directional and frequency adaptive feedback signalswithin bias itself to enhance the bias tolerance of the model. It consistsof two key components: (1) Anti-Drift Rectification (ADR). ADRtreats inference-time drift as a signal to learn the direction to steer devi-ated states back toward the target. ADR endows the model with intrin-sic active self-rectification capabilities; (2) Frequency Compensation(FC). Empirically, we observe that accumulated bias often stems from alack of low-frequency components in high-noise stages and exposure biascarries the missing frequency information. FC leverages the bias itself asa self-feedback weighting factor to reinforce the missing frequency com-ponents. Experiments on CIFAR-10, CelebA-64, and ImageNet-256/512show that DEFAR outperforms prior baselines and further demonstratesfavorable scalability, compatibility, and inference robustness. Code willbe made available in https://github.com/wuliwuliy/DEFAR.