Haridis, AlexandrosZhou, Charles2026-06-052026A. Haridis and C. Zhou, “Rethinking pretraining for specialized design data: evidence from the JONES-19 cultural design dataset.” In: Proceedings of the Twelfth International Conference on Design Computing and Cognition (DCC ’26), Ecoles de Mines, Paris, France.https://dash.harvard.edu/handle/1/42739841Design and architectural archives encode expert human knowledge in graphical formats, providing a critical testbed for design-inspired Machine Learning (ML) challenges absent with typical computer vision benchmarks. Building on JONES-19, a small-size image dataset based on The Grammar of Ornament (London, 1857), we evaluate the discriminative performance of Convolutional Neural Networks (CNNs) in two model training strategies: (a) ImageNet pretraining for domain-general “visual common sense,” and (b) learning from scratch on the design data in JONES-19. We find that while domain-general priors improve discriminative performance, learning from scratch augmented with repeated local sampling (multi-crop) effectively recovers these gains. For highly structured design data, local design-driven representations provide sufficient foundation for learning, challenging a reliance on massive general-purpose pretraining. These findings suggest that in specialized design domains, careful curation of smaller high-quality datasets that capture empirical and formal design principles may prove more effective and informative on the nature of a particular design domain than prioritizing large-scale data collection.EnglishDesign DatasetsMachine LearningDesign PerceptionArchitectural IntelligenceDesign ComputingConference Proceedings