Kwanghee Won

Group-Supervised Contrastive Learning for Semantic PCG Map Representations

A group-supervised contrastive learning framework that addresses semantic ambiguity through two complementary mechanisms that expands parameter-based templates into diverse natural language descriptions using Large Language Models, and a group-supervised multi-positive contrastive objective that aligns text embeddings with sets of maps generated under shared control parameters is introduced.