Knowledge Distillation - Transferring Knowledge to Smaller Models

Knowledge Distillation: Transferring Knowledge to Smaller Models

Knowledge distillation is a technique for compressing large, accurate models (teachers) into smaller, efficient models (students) while preserving performance. By training smaller models to mimic the behavior of larger ones, knowledge distillation enables deploying high-performance AI on resource-constrained devices.

What is Knowledge Distillation?

Transferring knowledge from models trained on large datasets to models

Distillation Strategies

Teacher model is pre-trained and fixed during student training. This is the most common approach, enabling one teacher to train multiple students. Offline distillation is straightforward but may not fully utilize teacher-student interaction.

Consider feature-based distillation for deeper knowledge transfer. Mat

Practical Guide: Implementing Knowledge Distillation

Step 1: Train or Obtain Teacher Model

Frequently asked questions