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.

提出了一种新方法来提高知识蒸馏效率，同时不需要资源密集的教师模型。通过使用紧凑的自动编码器提取特征并计算不同类别之间的相似度得分，然后对这些相似度得分应用softmax函数以获得软概率向量，这个向量在学生模型的训练中作为有价值的指导。在各种数据集上进行的广泛实验表明，与依赖大型教师模型的传统知识蒸馏方法相比，我们的方法在资源效率方面具有更高的优势，并在模型准确性方面实现了相似甚至更高的性能。

ReffAKD: 资源高效的基于自编码器的知识蒸馏