Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation
Demographic imbalance in synthetic face generation can propagate to downstream face recognition systems, making fairness an important consideration when diffusion models are used for data generation. Existing fairness-aware generation approaches often require model retraining, architectural modifications, or repeated guidance throughout the reverse diffusion process. In this work, we introduce Semantic Boundary Predictor (SBP), an inference-time framework that performs demographic guidance through a one-shot intervention during reverse denoising. Our approach is motivated by the observation th
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Learning Late, Guiding Early: Timestep-Decoupled Semantic Gu” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Learning Late, Guiding Early: Timestep-Decoupled Semantic Gu” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Subir Kumar Parida →
“Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation”
- LinkedLinked via arxiv author · 85%Rajbabu Velmurugan →
“Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation”
- LinkedLinked via arxiv author · 85%Ketan Kotwal →
“Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation”
- LinkedLinked via arxiv author · 85%R. S. Sengar →
“Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation”
- LinkedLinked via arxiv author · 85%Swati Hiremath →
“Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation”