Abstract:

Knowledge Distillation (KD) in Artificial Intelligence (AI) achieves model lightweighting through a teacher-student framework, emerging as a key technology to address the performance-efficiency bottleneck in deep learning. This paper systematically analyzes KD’s theoretical framework from the perspective of algorithm evolution, categorizing knowledge transfer paths into four paradigms: response-based, feature-based, relation-based, and structure-based. It establishes a comparative evaluation system for dynamic and static KD methods. We deeply explore innovative mechanisms such as cross-modal feature alignment, adaptive distillation architectures, and multi-teacher collaborative validation, while analyzing fusion strategies like progressive knowledge transfer and adversarial distillation. Through empirical analysis in computer vision and natural language processing, we assess KD’s practicality in scenarios like image classification, semantic segmentation, and text generation. Notably, we highlight KD’s potential in agriculture and geosciences, enabling efficient deployment in resource-constrained settings for precision agriculture and geospatial analysis. Current models often face issues like ambiguous knowledge selection mechanisms and insufficient theoretical interpretability. Accordingly, we discuss the feasibility of automated distillation systems and multimodal knowledge fusion, offering new technical pathways for edge intelligence deployment and privacy computing, particularly suited for agricultural intelligence and geoscience research.

Key words: knowledge distillation, model compression, knowledge transfer, dynamic optimization, multimodal learning