What are the advantages and limitations of knowledge distillation?

Knowledge distillation transfers capabilities from a large, complex model (teacher) to a smaller, simpler model (student), enabling deployment where resources are constrained. Its primary advantage is compressing models for efficient inference.

Key advantages include significant reduction in model size and computational demands, facilitating deployment on edge devices. The student can often achieve comparable or superior accuracy to training from scratch by learning from the teacher's softened outputs (soft labels) and internal representations. Major limitations involve the reliance on a pre-trained, high-performance teacher model, adding upfront cost and complexity. Distillation can also sometimes incur a small but noticeable accuracy loss compared to the teacher, though the student usually exceeds its standalone performance.

Knowledge distillation is widely applied in scenarios demanding efficient on-device AI, like mobile apps and embedded systems. Its core value lies in democratizing access to high-performance AI models by reducing inference costs without sacrificing significant accuracy.