A knowledge guided hyperdimensional computing framework for culturally compliant fashion design

Globalization and digital fashion growth demand intelligent systems capable of generating culturally compliant, personalized clothing designs. Conventional deep learning models struggle with semantic-symbolic decoupling, cultural misalignment, and few-shot adaptation, leading to aesthetic inconsistencies and cultural appropriation risks. This study proposes a hyperdimensional computing framework integrating quantized self-supervised meta-learning and differentiable fuzzy logic reasoning. Cultural semantics and visual symbols are embedded in a 10,000-dimensional vector space, enabling precise representation of cross-cultural styles. A hash-based quantized meta-learning framework supports discrete latent structures and robust generalization. Differentiable fuzzy logic formalizes cultural taboos as soft constraints, ensuring rule compliance without stifling creativity. Experiments demonstrate 91.3% cultural symbol accuracy and over 88% accuracy for complex styles, with personalized adaptation reaches 89.3% in 5-shot settings within 5 inference-time gradient steps of latent variable optimization. The proposed framework is comprehensively validated through robustness evaluation, expert-based real-world assessment, and multi-context application testing, forming a consistent evaluation pipeline. This approach establishes an explainable paradigm for culturally sensitive, low-resource global fashion design.