  Can we complete pre-training of Vision Transformers (ViT) without natural
images and human-annotated labels? Although a pre-trained ViT seems to heavily
rely on a large-scale dataset and human-annotated labels, recent large-scale
datasets contain several problems in terms of privacy violations, inadequate
fairness protection, and labor-intensive annotation. In the present paper, we
pre-train ViT without any image collections and annotation labor. We
experimentally verify that our proposed framework partially outperforms
sophisticated Self-Supervised Learning (SSL) methods like SimCLRv2 and MoCov2
without using any natural images in the pre-training phase. Moreover, although
the ViT pre-trained without natural images produces some different
visualizations from ImageNet pre-trained ViT, it can interpret natural image
datasets to a large extent. For example, the performance rates on the CIFAR-10
dataset are as follows: our proposal 97.6 vs. SimCLRv2 97.4 vs. ImageNet 98.0.
