Learning to Track at 100 FPS with Deep Regression Networks ECCV 2016 论文笔记 工程网页:http://davheld.github.io/GOTURN/GOTURN.html GitHub 地址:https://github.com/davheld/GOTURN 本文第一次利用 Deep Learning 技术将跟踪的速度维持在 100fps,当然是使用 GPU 的前提下.本文的流程框架如下所示: 将跟踪看做是回归问题,…
- 论文地址:https://arxiv.org/abs/1604.01325 contribution is twofold: (i) we leverage a ranking framework to learn convolution and projection weights that are used to build the region features; (ii) we employ a region proposal network to learn which regio…
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision. 学习目标 See deep neural networks as successive blocks put one after each other Build and train a deep L-layer Ne…
Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images. In…
声明:所有内容来自coursera,作为个人学习笔记记录在这里. Initialization Welcome to the first assignment of "Improving Deep Neural Networks". Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help…
Understanding, generalisation, and transfer learning in deep neural networks FEBRUARY 27, 2017 This is the first in a series of posts looking at the ‘top 100 awesome deep learning papers.’ Deviating from the normal one-paper-per-day format, I’ll ta…
Deep Learning论文笔记之(一)K-means特征学习 zouxy09@qq.com http://blog.csdn.net/zouxy09 自己平时看了一些论文,但老感觉看完过后就会慢慢的淡忘,某一天重新拾起来的时候又好像没有看过一样.所以想习惯地把一些感觉有用的论文中的知识点总结整理一下,一方面在整理过程中,自己的理解也会更深,另一方面也方便未来自己的勘察.更好的还可以放到博客上面与大家交流.因为基础有限,所以对论文的一些理解可能不太正确,还望大家不吝指正交流,…
The unstable gradient problem: The fundamental problem here isn't so much the vanishing gradient problem or the exploding gradient problem. It's that the gradient in early layers is the product of terms from all the later layers. When there are many…