论文标题
用于独特判别规范相关网络的GPU加速算法
A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network
论文作者
论文摘要
当前，基于深的神经网络（DNN）模型已引起了极大的关注，并已广泛用于不同的领域。但是，由于数据驱动的性质，DNN模型可能会在小型数据集上产生不满意的性能。为了解决这个问题，提出了一个独特的判别规范相关网络（DDCCANET）来生成深层特征表示形式，从而在图像分类上产生了改善的性能。但是，DDCCANET模型最初是在CPU上实现的，其计算时间与GPU上运行的最新DNN模型相当。在本文中，提出了一种基于GPU的加速算法，以进一步优化DDCCANET算法。结果，不仅可以保证ddccanet的性能，而且大大缩短了计算时间，使该模型更适用于实际任务。为了证明所提出的加速算法的有效性，我们在三个具有不同尺度的数据库上进行实验。实验结果证明了在给定示例中提出的加速算法的优越性。
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.
