Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for ...
Convolutional sparse codingConvolutional neural networkAdaptiveThe convolutional sparse coding-based super-resolution (CSC-SR) method has shown its good performance in single image super-resolution. It divides the low-resolution (LR) image into low-frequency part and the high-frequency part, and ...
introduced SRCNN (Dong et al., 2014), the first deep convolutional neural network for super-resolution reconstruction tasks. Subsequently, researchers proposed a large number of deep learning networks dedicated to improving the quality of super-resolution reconstruction and achieved good results. In ...
3 Scale-adaptive CNN 3.1. Ground truth density maps 每个人头我们使用一个 delta function 来表示,ground truth density map D(x) 由 delta function 和 一个 Gaussian kernel 卷积得到 N 表示图像中人头总数, The sum of the density map is equivalent to the total number of pedestrians in a crowd 3....
SENet加Condconv的结合-WeightNet: Revisiting the Design Space of Weight Networks githubgit开源http 地址:http://xxx.itp.ac.cn/pdf/2007.11823.pdf 西西嘛呦 2020/08/27 6700 记录一些论文 httphttps网络安全神经网络 1、Bag of Tricks and A Strong Baseline for Deep Person Re-identification 西西嘛呦 2020...
Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are investigated as biologically plausible and high-performance models of neural computation. The sparse and binary communication between spiking neurons potentially ena
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices 论文链接: https://arxiv.org/abs/1707.01083 一、 Problem Statement 提出一个适用于移动设备的backbone,shufflnet。 二、 Direction 提出了两个操作: poi... 【论文阅读笔记】MULTI-SCALE DENSE NETWORKS FOR RESOURCE EFFICIENT IM...
Abstract Spiking neural networks (SNNs) have attracted significant research attention due to their inherent sparsity and event-driven processing capabilities. Recent studies indicate that the incorporation of convolutional and residual structures into SNNs can substantially enhance performance. However, these ...
In Image processing: algorithms and systems, neural networks, and machine learning, vol. 6064, 354–365 (SPIE, 2006). Kostadin, D., Alessandro, F. & Karen, E. Video denoising by sparse 3d transform-domain collaborative filtering. In European signal processing conference 149, 2 (2007). ...
proposed a method named Smooth-AP [17] for end-to-end training of deep networks with a simple and elegant implementation, which improves the performance over the state-of-the-art in the image retrieval on many public datasets. Existing research works have demonstrated that deep learning can ...