  Quantization approximates a deep network model with floating-point numbers by
the one with low bit width numbers, in order to accelerate inference and reduce
computation. Quantizing a model without access to the original data, zero-shot
quantization can be accomplished by fitting the real data distribution by data
synthesis. However, zero-shot quantization achieves inferior performance
compared to the post-training quantization with real data. We find it is
because: 1) a normal generator is hard to obtain high diversity of synthetic
data, since it lacks long-range information to allocate attention to global
features; 2) the synthetic images aim to simulate the statistics of real data,
which leads to weak intra-class heterogeneity and limited feature richness. To
overcome these problems, we propose a novel deep network quantizer, dubbed
Long-Range Zero-Shot Generative Deep Network Quantization (LRQ). Technically,
we propose a long-range generator to learn long-range information instead of
simple local features. In order for the synthetic data to contain more global
features, long-range attention using large kernel convolution is incorporated
into the generator. In addition, we also present an Adversarial Margin Add
(AMA) module to force intra-class angular enlargement between feature vector
and class center. As AMA increases the convergence difficulty of the loss
function, which is opposite to the training objective of the original loss
function, it forms an adversarial process. Furthermore, in order to transfer
knowledge from the full-precision network, we also utilize a decoupled
knowledge distillation. Extensive experiments demonstrate that LRQ obtains
better performance than other competitors.
