Efficient Knowledge Distillation Extensions

Background: Knowledge distillation frameworks aim to transfer learned representations from larger, potentially specialized models (teachers) to smaller, more efficient models (students).

Question / Future Work: Future work includes exploring intermediate representation distillation, where the student model learns to mimic not just the final output logits but also internal representations from the teachers, and investigating parameter-efficient methods for sharing parameters across the specialized teacher models.