Improving data-free quantization with confidence-guided data synthesis

초록

Data-Free Quantization (DFQ) enables model quantization without real data. Therefore, the accuracy of DFQ is largely affected by generated samples. However, we find that confidence distributions predicted from full-precision model over real and synthetic samples are significantly dissimilar, and this distribution discrepancy undermines quantization accuracy. To resolve this, we present an entropy regularization during synthetic data generation to make it resemble to real one much more. Furthermore, we propose Scaled Logit Alignment during quantization-aware training to bridge the representational gap between models. Our method achieves superior performance compared to the recent DFQ methods on ViT, CNN, and object detector architectures. © 2026 The Authors.

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