ImageNet :经典的划时代的数据集 Deep Convolutional:深度卷积在当时还处于比较少提及的地位,当时主导的是传统机器学习算法 作者 一作Alex Krizhevsky 和二作 Ilya Sutskever 都是 2018 年因作为 “深度学习领域的三大先驱之一” 而获得图灵奖的 Geoffrey E. Hinton 辛老爷子的学生。值得一提的是,二作 Ilya Sutske...
《ImageNet Classification with Deep Convolutional Neural Networks》 剖析 CNN 领域的经典之作, 作者训练了一个面向数量为 1.2 百万的高分辨率的图像数据集ImageNet, 图像的种类为1000 种的深度卷积神经网络。并在图像识别的benchmark数据集上取得了卓越的成绩。 和之间的LeNet还是有着异曲同工之妙。这里涉及到 ca...
ImageNet Classification with Deep Convolutional Neural Networks论文解读 一、Abstract 我们训练了一个大型的深度卷积神经网络,将ImageNet LSVRC-2010竞赛中的120万张高分辨率图像分类为1000种不同的类别。 在测试数据上,我们实现了前1个和前5个错误率分别为37.5%和17.0%,这比以前的最新技术要好得多。 该神经网络具...
使用ReLUs(实线)的四层卷积神经网络在CIFAR-10上达到25%的训练错误率,比使用tanh神经元(虚线)的同等网络快六倍。每个网络的学习率都是独立选择的,以使训练尽可能快。没有任何形式的正规化。这里演示的效果的大小随网络结构的不同而不同,但是使用ReLUs的网络始终比使用饱和神经元的网络学习速度快几倍 第一,采用siq...
Due to dense pyramid nature, it is very effective to propagate the extracted feature from lower dilated convolutional layers (DCLs) to middle and higher DCLs, which result in better estimation accuracy. The FM is used to fuse the incoming features extracted by other modules. The proposed ...
of 2D convolution and all the other operations inherent in training convolutional neural networks,which we make available publicly1. Our network contains a number of new and unusual features which improve its performance and reduce its training time, which are detailed in Section 3. The size of ...
Instead of perfectly modeling outliers, which is rather challenging from a generative model perspective, we develop a deep convolutional neural network to capture the characteristics of degradation. We note directly applying existing deep neural networks does not produce reasonable results. Our solution ...
An accurate deep convolutional neural networks model for no-reference image quality assessment Convolutional neural networksThe goal of image quality assessment (IQA) is to use computational models to measure the consistency between image quality and ... B Bare,L Ke,Y Bo - IEEE 被引量: 0发表:...
of 2D convolution and all the other operations inherent in training convolutional neural networks, which we make available publicly. Our network contains a number of new and unusual features which improve its performance and reduce its training time, which are detailed in Section 3. The size of ...
四年前,当我们还在多伦多大学(University of Toronto)的时候,我们被称为“监督”(SuperVision)的深层神经网络使我们在自然图像中识别物体的错误率降低了近一半,并引发了计算机视觉领域迟来的范式转变。图4显示了一些监视功能的示例。监督是从20世纪80年代被广泛研究的多层神经网络发展而来的。这些网络使用了多层特征检测...