Can knowledge distillation make small models stronger?

Yes, knowledge distillation can significantly strengthen small models by transferring learned knowledge from larger, more complex "teacher" models to smaller "student" models. This technique enhances the student's capabilities while maintaining efficiency.

Key principles involve training the student to mimic the teacher's outputs, particularly softened probability distributions or logits, rather than just hard labels, which captures nuanced patterns. Necessary conditions include a high-performance pre-trained teacher model and compatible architecture design for the student. The approach is widely applicable in deep learning tasks like natural language processing and computer vision, but it requires careful tuning of hyperparameters like temperature in distillation loss. Precautions involve ensuring the teacher's knowledge is relevant and avoiding overfitting during training.

This method adds substantial value by enabling smaller models to approach the accuracy of large models, facilitating practical deployment on resource-constrained devices such as smartphones or edge systems. Implementation typically involves distilling logits during training alongside standard supervised learning, achieving faster inference and lower operational costs in business scenarios like real-time recommendation engines or on-device AI.