B. Wohlberg, "Efficient convolutional sparse coding," in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pp. 7173-7177, IEEE, 2014.B. Wohlberg, "Efficient convolutional sparse coding," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP),...
因此,SSNN-BP 使 BP 与纯基于脉冲的神经形态硬件兼容。 6 Unsupervised energy disaggregation via convolutional sparse coding 标题:通过卷积稀疏编码进行无监督能量分解 文章链接:https://arxiv.org/abs/2207.09785 摘要:在这项工作中,提出了一种在配备智能电表的私人家庭中进行无监督能源分解的方法。该方法旨在将功...
We generated synthetic textures using an iterative model that uses a convolutional neural network (VGG16) to extract a compact multi-scale representation of texture images59(Fig.1a). To disentangle the contribution of higher-order image statistics from lower-order ones, for each texture exemplar we...
Convolutional neural networks have become ubiquitous in computer vision ever since AlexNet [19] popularized deep convolutional neural networks by winning the ImageNet Challenge: ILSVRC 2012 [24]. The general trend has been to make deeper and more complicated networks in order to achieve higher accurac...
The deployment of deep convolutional neural networks (CNNs) in many Real-World applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; ...
一个简单解释是:"经过BN层的输出要经过的计算是\gamma*x+\beta,\gamma小输出就小,也就意味着这个输出对最终结果影响小",具体内容可参考《Learning Efficient Convolutional Networks through Network Slimming》的这篇论文原文以及知乎文章的评论区,我并没有细究...
Jpeg artifacts reduction via deep convolutional sparse coding. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 2501–2510, 2019. 8 [19] Siddhartha Gairola, Mayur Hemani, Ayush Chopra, and Bal- aji Krishnamurthy. Simpropnet: Imp...
The deployment of deep convolutional neural networks (CNNs) in many Real-World applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; ...
1. Single image super-resolution based on adaptive convolutional sparse coding and convolutional neural networks [J] . Zhao Jianwei, Chen Chen, Zhou Zhenghua, Journal of visual communication & image representation . 2019,第JANa期 机译:基于自适应卷积稀疏编码和卷积神经网络的单图像超分辨率 2. A...
Although convolutional representation of multiscale sparse tensor demonstrated its superior efficiency to accurately model the occupancy probability for the compression of geometry component of dense object point clouds, its capacity for representing sparse LiDAR point cloud geometry (PCG) was largely limited...