@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00235902,
 author = {ルイス, モルミレ and 渥美, 雅保},
 book = {第86回全国大会講演論文集},
 issue = {1},
 month = {Mar},
 note = {Traditional dataset building involves time-consuming tasks such as web scraping, cleaning, and labeling. Our proposed method utilizes a fast stable diffusion technique to efficiently generate synthetic images from text prompts, eliminating the need for manual data collection while mitigating biases and mislabeling. We conduct experiments with a vision transformer, comparing models trained on real datasets, datasets enhanced with synthetic images, and fully synthetic datasets. The results showcase the efficacy of stable diffusion-synthesized images in enhancing model generalization and accuracy, highlighting the potential of this approach in the realm of computer vision.},
 pages = {111--112},
 publisher = {情報処理学会},
 title = {Self-Supervised Pre-training of Vision Transformers Using Stable Diffusion-Generated Images},
 volume = {2024},
 year = {2024}
}