Knowledge From the Dark Side: Entropy-Reweighted Knowledge Distillation for Balanced Knowledge Transfer

Knowledge Distillation (KD) transfers knowledge from a larger "teacher" model to a compact "student" model, guiding the student with the "dark knowledge" $\unicode{x2014}$ the implicit insights present in the teacher's soft predictions. Although existing KDs have shown the potential of transferring knowledge, the gap between the two parties still exists. With a series of investigations, we argue the gap is the result of the student's overconfidence in prediction, signaling an imbalanced focus on pronounced features while overlooking the subtle yet crucial dark knowledge. To overcome this, we introduce the Entropy-Reweighted Knowledge Distillation (ER-KD), a novel approach that leverages the entropy in the teacher's predictions to reweight the KD loss on a sample-wise basis. ER-KD precisely refocuses the student on challenging instances rich in the teacher's nuanced insights while reducing the emphasis on simpler cases, enabling a more balanced knowledge transfer. Consequently, ER-KD not only demonstrates compatibility with various state-of-the-art KD methods but also further enhances their performance at negligible cost. This approach offers a streamlined and effective strategy to refine the knowledge transfer process in KD, setting a new paradigm in the meticulous handling of dark knowledge. Our code is available at https://github.com/cpsu00/ER-KD.