  Neural network quantization is a very promising solution in the field of
model compression, but its resulting accuracy highly depends on a
training/fine-tuning process and requires the original data. This not only
brings heavy computation and time costs but also is not conducive to privacy
and sensitive information protection. Therefore, a few recent works are
starting to focus on data-free quantization. However, data-free quantization
does not perform well while dealing with ultra-low precision quantization.
Although researchers utilize generative methods of synthetic data to address
this problem partially, data synthesis needs to take a lot of computation and
time. In this paper, we propose a data-free mixed-precision compensation
(DF-MPC) method to recover the performance of an ultra-low precision quantized
model without any data and fine-tuning process. By assuming the quantized error
caused by a low-precision quantized layer can be restored via the
reconstruction of a high-precision quantized layer, we mathematically formulate
the reconstruction loss between the pre-trained full-precision model and its
layer-wise mixed-precision quantized model. Based on our formulation, we
theoretically deduce the closed-form solution by minimizing the reconstruction
loss of the feature maps. Since DF-MPC does not require any original/synthetic
data, it is a more efficient method to approximate the full-precision model.
Experimentally, our DF-MPC is able to achieve higher accuracy for an ultra-low
precision quantized model compared to the recent methods without any data and
fine-tuning process.
