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Research questionHow can pixel-space flow-matching objectives avoid low-frequency dominance while learning fine image details?Natural images concentrate most signal energy in low spatial frequencies, while textures and edges occupy sparse high-frequency bands. Uniform pixel losses therefore provide a stronger optimization signal for coarse structure than for fine detail.
AI
Image Generation
Machine Learning
Latest papersRecent research connected to this question, newest first.Balancing Frequencies and Pixels in Flow MatchingThis applies to natural-image generation with pixel-space flow models trained using reconstruction-style objectives. The source reports evidence across multiple model scales, including faster convergence and improved FID and perceptual fidelity, but does not specify particular datasets or architectures.research paper · Sep 2, 2026
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