  The deep neural networks used in modern computer vision systems require
enormous image datasets to train them. These carefully-curated datasets
typically have a million or more images, across a thousand or more distinct
categories. The process of creating and curating such a dataset is a monumental
undertaking, demanding extensive effort and labelling expense and necessitating
careful navigation of technical and social issues such as label accuracy,
copyright ownership, and content bias.
  What if we had a way to harness the power of large image datasets but with
few or none of the major issues and concerns currently faced? This paper
extends the recent work of Kataoka et. al. (2020), proposing an improved
pre-training dataset based on dynamically-generated fractal images. Challenging
issues with large-scale image datasets become points of elegance for fractal
pre-training: perfect label accuracy at zero cost; no need to store/transmit
large image archives; no privacy/demographic bias/concerns of inappropriate
content, as no humans are pictured; limitless supply and diversity of images;
and the images are free/open-source. Perhaps surprisingly, avoiding these
difficulties imposes only a small penalty in performance. Leveraging a
newly-proposed pre-training task -- multi-instance prediction -- our
experiments demonstrate that fine-tuning a network pre-trained using fractals
attains 92.7-98.1% of the accuracy of an ImageNet pre-trained network.
