Model quantization, which aims to compress deep neural networks and
accelerate inference speed, has greatly facilitated the development of
cumbersome models on mobile and edge devices. There is a common assumption in
quantization methods from prior works that training data is available. In
practice, however, this assumption cannot always be fulfilled due to reasons of
privacy and security, rendering these methods inapplicable in real-life
situations. Thus, data-free network quantization has recently received
significant attention in neural network compression. Causal reasoning provides
an intuitive way to model causal relationships to eliminate data-driven
correlations, making causality an essential component of analyzing data-free
problems. However, causal formulations of data-free quantization are inadequate
in the literature. To bridge this gap, we construct a causal graph to model the
data generation and discrepancy reduction between the pre-trained and quantized
models. Inspired by the causal understanding, we propose the Causality-guided
Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on
data via approaching an equilibrium of causality-driven intervened
distributions. Specifically, we design a content-style-decoupled generator,
synthesizing images conditioned on the relevant and irrelevant factors; then we
propose a discrepancy reduction loss to align the intervened distributions of
the pre-trained and quantized models. It is worth noting that our work is the
first attempt towards introducing causality to data-free quantization problem.
Extensive experiments demonstrate the efficacy of Causal-DFQ. The code is
available at this https URL

通过构建因果图来模拟预训练模型和量化模型之间的数据生成和差异减少，提出了因果引导的无数据网络量化方法 Causal-DFQ，通过接近因果驱动的干预分布的均衡状态来消除对数据的依赖。