一.文献名字和作者     Convolutional Neural Networks at Constrained Time Cost,CVPR 2015 二.阅读时间      2015年6月30日 三.文献的目的     作者希望在保持计算复杂度的前提下,通过改动模型深度和卷积模板的參数来提高CNN的准确率.作者通过大量的实验来找到网络结构中不同的參数的重要性,并在ImageNet2012数据集上面取得有竞争力的效果. 四.文献的贡献点     作者的贡献主要在于通过各种对照实验来说明不同…
Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally, it uses the following new Theano functions and concepts: T.tanh, shared variables, basic arithme…
这是Jake Bouvrie在2006年写的关于CNN的训练原理,虽然文献老了点,不过对理解经典CNN的训练过程还是很有帮助的.该作者是剑桥的研究认知科学的.翻译如有不对之处,还望告知,我好及时改正,谢谢指正! Notes on Convolutional Neural Networks Jake Bouvrie 2006年11月22 1引言 这个文档是为了讨论CNN的推导和执行步骤的,并加上一些简单的扩展.因为CNN包含着比权重还多的连接,所以结构本身就相当于实现了一种形式的正则化了.另外CN…
The Impact of Imbalanced Training Data for Convolutional Neural Networks Paulina Hensman and David Masko 摘要 本论文从实验的角度调研了训练数据的不均衡性对采用CNN解决图像分类问题的性能影响.CIFAR-10数据集包含10个不同类别的60000个图像,用来构建不同类间分布的数据集.例如,一些训练集中包含一个类别的图像数目与其他类别的图像数目比例失衡.用这些训练集分别来训练一个CNN,度量其得…
Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do reali…
This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, discuss our model and its possible applications to Flipboa…
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Deep Learning & Art: Neural Style Transfer Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). In this as…
Convolutional Neural Networks: Application Welcome to Course 4's second assignment! In this notebook, you will: Implement helper functions that you will use when implementing a TensorFlow model Implement a fully functioning ConvNet using TensorFlow (…
Convolutional Neural Networks: Step by Step Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagati…