  Data-free quantization aims to achieve model quantization without accessing
any authentic sample. It is significant in an application-oriented context
involving data privacy. Converting noise vectors into synthetic samples through
a generator is a popular data-free quantization method, which is called
generative data-free quantization. However, there is a difference in attention
between synthetic samples and authentic samples. This is always ignored and
restricts the quantization performance. First, since synthetic samples of the
same class are prone to have homogenous attention, the quantized network can
only learn limited modes of attention. Second, synthetic samples in eval mode
and training mode exhibit different attention. Hence, the batch-normalization
statistics matching tends to be inaccurate. ACQ is proposed in this paper to
fix the attention of synthetic samples. An attention center position-condition
generator is established regarding the homogenization of intra-class attention.
Restricted by the attention center matching loss, the attention center position
is treated as the generator's condition input to guide synthetic samples in
obtaining diverse attention. Moreover, we design adversarial loss of paired
synthetic samples under the same condition to prevent the generator from paying
overmuch attention to the condition, which may result in mode collapse. To
improve the attention similarity of synthetic samples in different network
modes, we introduce a consistency penalty to guarantee accurate BN statistics
matching. The experimental results demonstrate that ACQ effectively improves
the attention problems of synthetic samples. Under various training settings,
ACQ achieves the best quantization performance. For the 4-bit quantization of
Resnet18 and Resnet50, ACQ reaches 67.55% and 72.23% accuracy, respectively.
