  Previous logits-based Knowledge Distillation (KD) have utilized predictions
about multiple categories within each sample (i.e., class predictions) and have
employed Kullback-Leibler (KL) divergence to reduce the discrepancy between the
student and teacher predictions. Despite the proliferation of KD techniques,
the student model continues to fall short of achieving a similar level as
teachers. In response, we introduce a novel and effective KD method capable of
achieving results on par with or superior to the teacher models performance. We
utilize teacher and student predictions about multiple samples for each
category (i.e., batch predictions) and apply cosine similarity, a commonly used
technique in Natural Language Processing (NLP) for measuring the resemblance
between text embeddings. This metric's inherent scale-invariance property,
which relies solely on vector direction and not magnitude, allows the student
to dynamically learn from the teacher's knowledge, rather than being bound by a
fixed distribution of the teacher's knowledge. Furthermore, we propose a method
called cosine similarity weighted temperature (CSWT) to improve the
performance. CSWT reduces the temperature scaling in KD when the cosine
similarity between the student and teacher models is high, and conversely, it
increases the temperature scaling when the cosine similarity is low. This
adjustment optimizes the transfer of information from the teacher to the
student model. Extensive experimental results show that our proposed method
serves as a viable alternative to existing methods. We anticipate that this
approach will offer valuable insights for future research on model compression.
