Data-Free Quantization via Pseudo-label Filtering

Quantization for model compression can efficiently re-duce the network complexity and storage requirement, but the original training data is necessary to remedy the performance loss caused by quantization. The Data-Free Quan-tization (DFQ) methods have been proposed to handle the absence of original training data with synthetic data. How-ever, there are differences between the synthetic and orig-inal training data, which affects the performance of the quantized network, but none of the existing methods con-siders the differences. In this paper, we propose an efficient data-free quantization via pseudo-label filtering, which is the first to evaluate the synthetic data before quantization. We design a new metric for evaluating synthetic data using self-entropy, which indicates the reliability of synthetic data. The synthetic data can be categorized with the met-ric into high- and low-reliable datasets for the following training process. Besides, the multiple pseudo-labels are designed to label the synthetic data with different reliabil-ity, which can provide valuable supervision information and avoid misleading training by low-reliable samples. Exten-sive experiments are implemented on several datasets, in-cluding CIFAR-10, CIFAR-100, and ImageNet with various models. The experimental results show that our method can perform excellently and outperform existing methods in ac-curacy.