Hyperdimensional Intelligent Engine for Personalized and Culturally Compliant Fashion Design

Globalization and the rapid growth of digital fashion demand intelligent systems capable of generating culturally compliant, personalized clothing designs across diverse traditions. Conventional deep learning models often rely on large labeled datasets and struggle with semantic–symbolic decoupling, cultural misalignment, and few-shot adaptation, resulting in aesthetic inconsistencies and risks of cultural appropriation. To overcome these limitations, a hyperdimensional intelligent engine is proposed that combines hyperdimensional computing, quantized self-supervised meta-learning, and differentiable fuzzy logic reasoning. Cultural semantics and visual symbols are embedded in a 10,000-dimensional vector space, where reversible binding and superposition enable precise, interpretable representation of complex cross-cultural styles.