  In this research, we propose an innovative method to boost Knowledge
Distillation efficiency without the need for resource-heavy teacher models.
Knowledge Distillation trains a smaller ``student'' model with guidance from a
larger ``teacher'' model, which is computationally costly. However, the main
benefit comes from the soft labels provided by the teacher, helping the student
grasp nuanced class similarities. In our work, we propose an efficient method
for generating these soft labels, thereby eliminating the need for a large
teacher model. We employ a compact autoencoder to extract essential features
and calculate similarity scores between different classes. Afterward, we apply
the softmax function to these similarity scores to obtain a soft probability
vector. This vector serves as valuable guidance during the training of the
student model. Our extensive experiments on various datasets, including
CIFAR-100, Tiny Imagenet, and Fashion MNIST, demonstrate the superior resource
efficiency of our approach compared to traditional knowledge distillation
methods that rely on large teacher models. Importantly, our approach
consistently achieves similar or even superior performance in terms of model
accuracy. We also perform a comparative study with various techniques recently
developed for knowledge distillation showing our approach achieves competitive
performance with using significantly less resources. We also show that our
approach can be easily added to any logit based knowledge distillation method.
This research contributes to making knowledge distillation more accessible and
cost-effective for practical applications, making it a promising avenue for
improving the efficiency of model training. The code for this work is available
at, https://github.com/JEKimLab/ReffAKD.
