  Recent advancements in low-cost ensemble learning have demonstrated improved
efficiency for image classification. However, the existing low-cost ensemble
methods show relatively lower accuracy compared to conventional ensemble
learning. In this paper, we propose a new low-cost ensemble learning, which can
simultaneously achieve high efficiency and classification performance. A CNN is
transformed into a multi-branch structure without introduction of additional
components, which maintains the computational complexity as that of the
original single model and also enhances diversity among the branches' outputs
via sufficient separation between different pathways of the branches. In
addition, we propose a new strategy that applies grouped convolution in the
branches with different numbers of groups in different branches, which boosts
the diversity of the branches' outputs. For training, we employ knowledge
distillation using the ensemble of the outputs as the teacher signal. The high
diversity among the outputs enables to form a powerful teacher, enhancing the
individual branch's classification performance and consequently the overall
ensemble performance. Experimental results show that our method achieves
state-of-the-art classification accuracy and higher uncertainty estimation
performance compared to previous low-cost ensemble methods. The code is
available at https://github.com/hjdw2/SEMBG.
