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Diversifying Sample Generation for Accurate Data-Free Quantization

CVPR 2021 · Architectures and efficiency › Quantization · approach: Post-training weight quantization · 87 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add DSG-generated diversified calibration dataZeroQ data generation for ResNet-18 quantization · ImageNet, ResNet-18 W4A4Top-1 accuracy: 26.04 -> 34.53Helped
Add DSG-generated diversified calibration dataZeroQ data generation for SqueezeNext quantization · ImageNet, SqueezeNext W6A6Top-1 accuracy: 39.83 -> 60.50Helped
Add DSG-generated diversified calibration dataZeroQ data generation for InceptionV3 quantization · ImageNet, InceptionV3 W4A4Top-1 accuracy: 12.00 -> 34.89Helped
Add Slack Distribution Alignment (SDA) (ablation)DSG, ResNet-18 · ImageNet W4A4Top-1 accuracy: 26.04 -> 33.39Helped
Add Layerwise Sample Enhancement (LSE) (ablation)DSG, ResNet-18 · ImageNet W4A4Top-1 accuracy: 26.04 -> 27.12Helped
Combine SDA and LSE (ablation)DSG, ResNet-18 · ImageNet W4A4Top-1 accuracy: 26.04 -> 34.53Helped
Increase SDA relaxation percentile from 0.9 to 1 (ablation)DSG, ResNet-18 · ImageNet W4A4Top-1 accuracyHurt
Use DSG rather than ZeroQ-generated dataAdaRound, ResNet-18 · ImageNet W3A32Top-1 accuracy: 49.86 -> 56.09Helped
Add labels to DSG-generated data (ablation)AdaRound, ResNet-18 · ImageNet W3A32Top-1 accuracy: 56.09 -> 58.27Helped
Add image prior to labeled DSG-generated data (ablation)AdaRound, ResNet-18 · ImageNet W3A32Top-1 accuracy: 58.27 -> 61.32Helped

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