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                          科學研究

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                          Diversifying Sample Generation for Accurate Data-Free Quantization

                          發表會議及期刊:CVPR

                          Xiangguo Zhang*1, Haotong Qin* 1, Yifu Ding1, Ruihao Gong3, 4,

                          Qinghua Yan1, Renshuai Tao1, Yuhang Li2, Fengwei Yu3, 4, Xianglong Liu1?

                          1Beihang University 2Yale University 3SenseTime Research 4Shanghai AI Laboratory

                          {xiangguozhang, zjdyf, yanqh, rstao}@buaa.edu.cn, yuhang.li@yale.edu,

                          {qinhaotong, xlliu}@nlsde.buaa.edu.cn, {gongruihao, yufengwei}@sensetime.com

                          Abstract:

                          Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quantized model according to the batch normalization (BN) statistics of FP32 ones and significantly relieves the heavy dependency on real training data in traditional quantization methods. Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model. We propose Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization. Specifically, we slack the alignment of feature statistics in the BN layer to relax the constraint at the distribution level and design a layerwise enhancement to reinforce specific layers for different data samples. Our DSG scheme is versatile and even able to be applied to the state-of-the-art post-training quantization method like AdaRound. We evaluate the DSG scheme on the large-scale image classification task and consistently obtain significant improvements over various network architectures and quantization methods, especially when quantized to lower bits (e.g., up to 22% improvement on W4A4). Moreover, benefiting from the enhanced diversity, models calibrated with synthetic data perform close to those calibrated with real data and even outperform them on W4A4.

                          comm@pjlab.org.cn

                          上海市徐匯區云錦路701號西岸國際人工智能中心37-38層

                          滬ICP備2021009351號-1

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                                                  韩国伦理电影