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.

中文翻译：

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没有自然图像的预训练

是否可以使用未经任何自然图像预训练的卷积神经网络来帮助自然图像理解？本文提出了一个新概念，即公式驱动的监督学习。我们通过分配分形来自动生成图像模式及其类别标签，这些分形基于现实世界背景知识中存在的自然规律。从理论上讲，在预训练阶段使用自动生成的图像而不是自然图像可以使我们生成标记图像的无限比例数据集。尽管使用建议的Fractal数据库（FractalDB）（没有自然图像的数据库）进行预训练的模型不一定在所有设置下都优于通过人工注释数据集进行预训练的模型，但我们能够部分超越ImageNet / Places的准确性训练的模型。