Knowledge Distillation is one of the key methods for downsizing AI models without a significant drop in accuracy, but high training costs have always been an obstacle to its widespread use.
New approaches focus on reducing the computational burden during the transfer of knowledge from large to small models to make this technique economical and implementable at scale for all businesses. With these optimizations, lighter models will replace heavy models in operational environments more quickly.