深度学习方法的出现解决了这一问题,其中卷积神经网络(CNNs)被证明在大规模视觉识别任务中非常有效。 2.研究内容: 本文介绍了一个基于卷积神经网络的深度学习模型,名为AlexNet。该模型通过在大规模视觉识别挑战(ILSVRC)上获得了最好的成绩,使得深度学习在视觉识别领域受到了广泛的关注。 3.研究方法: AlexNet是一个由8...
The deployment of deep convolutioal neural networks (CNNs) in many real world applications is largely hindered by their high computional cost. 深度卷积神经网络在许多现实世界应用中的部署在很大程度上受到其高计算成本的阻碍。本文,提出一种新方法用于CNN:1)减小模型大小,2)减少运行时的内存占用,以及3)在...
最近,卷积神经网络(CNNs)在各种计算机视觉任务中取得了巨大成功,尤其是在与识别相关的任务中。 我们提出并比较了两种架构:一种是通用架构,另一种是包含一个将不同图像位置的特征向量相关联的层的架构 由于现有的 ground truth 数据集的规模不足以训练 CNN,因此我们生成了一个合成的飞椅数据集。我们的研究表明,在...
they have still been prohibitively expensive to apply in large scale to high-resolution images. Luckily, current GPUs, paired with a highly-optimized implementation of 2D convolution, are powerful enough to facilitate the training of interestingly-large CNNs, and recent datasets ...
(namely, stationarity of statistics and locality of pixel dependencies). Thus, compared to standard feedforward neural networks with similarly-sized layers, CNNs have much fewer connections and parameters and so they are easier to train, while their theoretically-best performance is likely to be ...
卷积神经网络(convolutional neural networks, CNNs)[2, 4, 15]利用类别标签学习弱监督的零件模型取得了显著的进展,这些类别标签不依赖于边界框/零件标注,因此可以大大提高细粒度识别的可用性和可伸缩性[25,31,35]。该框架通常由两个独立的步骤组成:1)通过正/负图像块[35]的训练进行局部定位,或者通过预先训练好...
深度学习论文阅读图像分类篇(一):AlexNet《ImageNet Classification with Deep Convolutional Neural Networks》 Abstract 摘要 1.Introduction 引言 2.The Dataset 数据集 3.The Architecture 架构 3.1 非线性ReLU 函数 3.2在多 GPU 上训练 3.3局部响应归一化 ...
这篇论文是剖析 CNN 领域的经典之作,也是入门 CNN 的必读论文。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。 ImageNet Classification with Deep Convolutional Neural Networks基于深卷积神经网络的图像网络分类 ...
Enough buildup. Let’s get into CNNs! 3. Convolutions What are Convolutional Neural Networks? They’re basically just neural networks that useConvolutional layers, a.k.a. Conv layers, which are based on the mathematical operation ofconvolution. Conv layers consist of a set offilters, which ...
The convolutional neural networks (CNNs) (LeCun et al., 1998) are a type of deep models in which trainable ?lters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. It has been shown that, ...