Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching
arXiv:2606.28226v1 Announce Type: cross Abstract: Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference. Existing mitigation strategies typically rely on static constraints or external heuristics. In this work, we propose that exposure bias itself inherently contains dynamic signals that can guide its own rectification. To leverage this, we introduce DEFAR (DirEctional-Frequency Adaptive Rectification). This framework simulates the single-step inference process during training to identify exposur
The paper was published on arXiv, representing a new academic development in the field of generative AI research.
Improving Flow Matching models addresses a core limitation (exposure bias) in generative AI, potentially leading to more stable and effective models for various applications.
This research provides a novel, self-rectifying mechanism for overcoming exposure bias in Flow Matching models, offering a path to more robust generative AI development.
- · AI researchers
- · Generative AI developers
- · Companies using Flow Matching models
- · Inefficient generative AI methods
- · Developers solely relying on static mitigation strategies
Improved stability and performance of generative models built on Flow Matching architecture.
Faster development and deployment of generative AI applications due to reduced need for manual bias correction.
Enhanced realism and diversity in AI-generated content across various domains, from image synthesis to scientific modeling.
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Read at arXiv cs.AI