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.全球化浪潮与数字时尚的迅猛发展，亟需能够适配多元文化传统、生成符合文化规范的个性化服饰设计的智能系统。传统深度学习模型往往依赖大规模标注数据集，且在语义-符号解耦、文化错位与少样本适配等方面存在瓶颈，易引发审美不一致问题，甚至存在文化挪用风险。为克服上述局限，本研究提出一款超维智能引擎(hyperdimensional intelligent engine)，其融合了超维计算(hyperdimensional computing)、量化自监督元学习以及可微模糊逻辑推理技术。文化语义与视觉符号被嵌入至10000维向量空间中，借助可逆绑定与叠加操作，可实现复杂跨文化风格的精准且可解释的表征。