Pre-training without Natural Images

Hirokatsu Kataoka; Kazushige Okayasu; Asato Matsumoto; Eisuke Yamagata; Ryosuke Yamada; Nakamasa Inoue; Akio Nakamura; Yutaka Satoh

arXiv · 2101.08515

Pre-training without Natural Images

Hirokatsu Kataoka·Kazushige Okayasu·Asato Matsumoto·Eisuke Yamagata·Ryosuke Yamada·Nakamasa Inoue·Akio Nakamura·Yutaka Satoh

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Abstract

Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven Supervised Learning. We automatically generate image patterns and their category labels by assigning fractals, which are based on a natural law existing in the background knowledge of the real world. Theoretically, the use of automatically generated images instead of natural images in the pre-training phase allows us to generate an infinite scale dataset of labeled images. Although the models pre-trained with the proposed Fractal DataBase (FractalDB), a database without natural images, does not necessarily outperform models pre-trained with human annotated datasets at all settings, we are able to partially surpass the accuracy of ImageNet/Places pre-trained models. The image representation with the proposed FractalDB captures a unique feature in the visualization of convolutional layers and attentions.

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Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh. 2021-01-21. Pre-training without Natural Images. https://arxiv.org/abs/2101.08515

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