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University of Electronic Science and Technology of China
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Achieving over 20% performance improvement in data-free knowledge distillation, UniDFKD eliminates reliance on architecture-specific priors, paving the way for more robust model training across diverse architectures.
Dual-Ambiguity Rectification can enhance image restoration performance by disentangling complex degradation cues, leading to cleaner outputs and fewer artifacts.
