Specialized Design Data Challenges General ImageNet Pretraining

A study re-examines the role of pretraining for Convolutional Neural Networks (CNNs) when applied to highly specialized design and architectural data. Using the JONES-19 dataset, which features graphical formats encoding expert human knowledge, researchers compared two training strategies: pretraining on ImageNet for general visual understanding versus training from scratch directly on the design data. The findings indicate that while domain-general priors from ImageNet pretraining do enhance discriminative performance, these gains can be effectively matched by training from scratch when augmented with repeated local sampling (multi-crop). This suggests that for data with strong inherent structure, such as design archives, carefully curated smaller datasets that embody specific design principles might be more informative and efficient than simply relying on vast, general-purpose pretraining datasets. This challenges the conventional wisdom of always prioritizing large-scale general pretraining, especially in specialized domains where domain-specific representations can provide a sufficient foundation for learning.