Pre-training without Natural Images
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.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2021
- DOI:
- arXiv:
- arXiv:2101.08515
- Bibcode:
- 2021arXiv210108515K
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning
- E-Print:
- ACCV 2020 Best Paper Honorable Mention Award, Codes are publicly available: https://github.com/hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch