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This work aims to address the challenges of training generative models, specifically reducing unrealistic image generation or hallucinations.

Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling

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This work aims to address the challenges of training generative models, specifically reducing unrealistic image generation or hallucinations.
Explored the concept of flow matching in generative image models.
Proposed an iterative refinement process to enhance image generation robustness.
Demonstrated the process's integration into various generative modeling techniques.
Flow matching was shown to produce hallucinations in the image generation process.
The iterative method led to improved quality and realism of generated images.
Robust performance enhancements were noted across different generative modeling frameworks.

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Iterative process enhances image generation quality in generative models, implying better visual outputs.

Haber et al. (2026) studied this question.

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