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.
Ye et al. (2026) studied this question.