While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from this limitation. To alleviate this limitation, in this paper, we leverage the synthetic data introduced by zero-shot quantization with calibration dataset and we propose a fine-grained data distribution alignment (FDDA) method to boost the performance of post-training quantization. The method is based on two important properties of batch normalization statistics (BNS) we observed in deep layers of the trained network, i.e., inter-class separation and intra-class incohesion. To preserve this fine-grained distribution information: 1) We calculate the per-class BNS of the calibration dataset as the BNS centers of each class and propose a BNS-centralized loss to force the synthetic data distributions of different classes to be close to their own centers. 2) We add Gaussian noise into the centers to imitate the incohesion and propose a BNS-distorted loss to force the synthetic data distribution of the same class to be close to the distorted centers. By introducing these two fine-grained losses, our method shows the state-of-the-art performance on ImageNet, especially when the first and last layers are quantized to low-bit as well. Our project is available at https://github.com/viperit/FDDA.

本文利用零样本量化所介绍的合成数据及校准数据集，并提出了细粒度数据分布对齐（FDDA）方法，以提高后训练量化的性能。该方法基于深层网络中批量规范化统计量（BNS）观察到的两个重要属性，即类间分离和类内不凝聚，通过计算校准数据集的每一类BNS作为每一类的中心并提出中心化损失、在其中加入高斯噪声来模拟不凝聚和提出畸变损失。应用这两种细粒度损失函数，本方法在ImageNet上展现出最先进的性能，特别是在将第一层和最后一层量化为低位时。

用于后训练量化的细粒度数据分布对齐