A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network

TL;DR

This paper introduces a GPU-accelerated algorithm for the DDCCANet model, significantly reducing computation time and enhancing its applicability for image classification tasks.

Contribution

The paper presents a novel GPU-based acceleration method for DDCCANet, improving its efficiency without compromising performance.

Findings

Accelerated DDCCANet achieves faster computation times.

Experimental results show improved performance on multiple datasets.

GPU implementation makes DDCCANet more practical for real-world applications.

Abstract

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