Image Style Transfer Using Convolutional Neural Networks 。先介绍文章和Model,然后会试着自己在Tensorflow实现一下。 摘要 不同风格图像的语义内容一直是很难表达的,因为缺乏对这一概念的直观描述(representation)来将“style”这一概念从一副图中剥离出来,即区分何为style何为content。我们用的模型基础是CNN以及一种...
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...
《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 :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...
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 ...
我对此保留疑问。作者没有证明这个说法。 本文在对数据集操作时,都各自使用了不同的数据增强。因此,针对具体的医学图像,要选用相应的数据增强方式,才能避免一些误差。 论文链接 Convolutional neural networks for medical image analysis: Full training or fine tuning?
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 ...
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 ...
Image style transfer using convolutional neural networks阅读笔记。 。 作者的官方开源:https://github.com/leongatys/PytorchNeuralStyleTransfer, ,是Pytorch版本O(∩_∩)O。 关键字 风格迁移,图像合成,神经网络,深度学习,机器学习 ...
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 ...