AI Research
CARE: Condition-Aware Representation Regularization for Diffusion Models
A breakthrough approach to enhancing diffusion model performance through condition-aware regularization.
Recent advances in diffusion models highlight the importance of representation regularization for improving sample quality and training efficiency. However, commonly used regularization methods often overlook the built-in conditions (such as labels or texts) which directly determ...
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ArXiv CS.LG
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ArXiv CS.LG
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CARE addresses the disconnect between static regularization and dynamic condition-based generation in diffusion models. By integrating conditioning data into the learning process, it significantly elevates image fidelity and training efficiency.
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