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Currently, deep neural networks (DNNs)-based models have drawn enormousattention and have been utilized to different domains widely. However, due tothe data-driven nature, the DNN models may generate unsatisfying performance onthe small scale data sets. To address this problem, a distinct discriminantcanonical correlation network (DDCCANet) is proposed to generate the deep-levelfeature representation, producing improved performance on image classification.However, the DDCCANet model was originally implemented on a CPU with computingtime on par with state-of-the-art DNN models running on GPUs. In this paper, aGPU-based accelerated algorithm is proposed to further optimize the DDCCANetalgorithm. As a result, not only is the performance of DDCCANet guaranteed, butalso greatly shortens the calculation time, making the model more applicable inreal tasks. To demonstrate the effectiveness of the proposed acceleratedalgorithm, we conduct experiments on three database with different scales.Experimental results validate the superiority of the proposed acceleratedalgorithm on given examples.

A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network