  Data-free quantization (DFQ) is a technique that creates a lightweight
network from its full-precision counterpart without the original training data,
often through a synthetic dataset. Although several DFQ methods have been
proposed for vision transformer (ViT) architectures, they fail to achieve
efficacy in low-bit settings. Examining the existing methods, we identify that
their synthetic data produce misaligned attention maps, while those of the real
samples are highly aligned. From the observation of aligned attention, we find
that aligning attention maps of synthetic data helps to improve the overall
performance of quantized ViTs. Motivated by this finding, we devise MimiQ, a
novel DFQ method designed for ViTs that focuses on inter-head attention
similarity. First, we generate synthetic data by aligning head-wise attention
responses in relation to spatial query patches. Then, we apply head-wise
structural attention distillation to align the attention maps of the quantized
network to those of the full-precision teacher. The experimental results show
that the proposed method significantly outperforms baselines, setting a new
state-of-the-art performance for data-free ViT quantization.
