第一种形式的数据增强包括生成图像平移和水平反射。我们通过从256×256图像中随机提取224×224块(及其水平反射)并在这些提取的块上训练我们的网络来做到这一点。这将我们训练集的大小增加了2048倍,尽管由此产生的训练示例当然是高度相互依赖的。如果没有这种方案,我们的网络会遭受严重的过拟合,这将迫使我们使用小得多...
We wrote a highly-optimized GPU implementation of 2D convolution and all the other operations inherent in 1 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,...
NIPS-2012-imagenet-classification-with-deep-convolutional-neural-networks-Paper.pdf 0x01 Abstract 训练一个deep convolutional nerual network来区分ImageNet的LSVRC-2010比赛中的120万张 high-resolution到1000个不同的class (网络效果)在我们的test中,我们错误率从37.5%到17%的提升,显著的好于现有的SOTA (网络结...
particularly convolutional neural networks (CNNs), have become the state-of-the-art approach for various tasks such as image classification21, image segmentation22, image denoising23, image restoration24, image deconvolution25, and image super-resolution26. These methods, which involve adjusting the w...
Convolutional neural networks are the first deep learning models that received a lot of attention due to their impressive performance in applications of computer vision. The main idea behind convolutional neural networks is to extract local features from the data. In a convolutional layer, the similar...
On the other hand, fully-convolutional neural networks can represent a way to scale the proposed approach to a whole oilfield. Experiments with whole and real cases are retained for future work. Proposed metamodelling approach is based on the idea of forecasting in latent variable space. The ...
论文泛读 | ImageNet Classification with Deep Convolutional Neural Networks 论文链接:https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf Q1:解决了什么? 目前主要利用机器学习来解决目标识别任务; 机器学习可以通过“扩充数据集”、“强化训练模型”、“充实预防过拟合的手段...
论文地址:http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf 2012年,Alex Krizhevsky发表了AlexNet,它是LeNet的一种更深更宽的版本,网络扩大(5个卷积层+3个全连接层+1个softmax层)并以显著的优势赢得了困难的ImageNet竞赛。
2 The Dataset 3 The Architecture 3.1 ReLU:f(x) = max(0, x) 在深度卷积神经网络中ReLU比tanh训练时间更快 Figure 1: A four-layer convolutional neural network with ReLUs (solid line) reaches a 25% training error rate on CIFAR-10 six times faster than an equivalent network with tanh neurons...
Convolutional neural networks (CNNs)We're now going to move onto the second artificial neural network, Convolutional Neural Networks (CNNs). In this section, we're going solve the same MNIST digit classification problem, instead this time using CNNs.Figure...