文档章节

人工智能资料库:第45辑(20170418)

AllenOR灵感
 AllenOR灵感
发布于 2017/09/10 01:19
字数 890
阅读 2
收藏 0

1.【论文 & 代码】The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

简介:

State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.

Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.

In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid
and Gatech 1 , without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.

原文链接:https://github.com/0bserver07/One-Hundred-Layers-Tiramisu

论文链接:https://arxiv.org/abs/1611.09326


2.【博客】Explanation of One-shot Learning with Memory-Augmented Neural Networks

简介:

I've found that the overwhelming majority of online information on artificial intelligence research falls into one of two categories: the first is aimed at explaining advances to lay audiences, and the second is aimed at explaining advances to other researchers. I haven't found a good resource for people with a technical background who are unfamiliar with the more advanced concepts and are looking for someone to fill them in. This is my attempt to bridge that gap, by providing approachable yet (relatively) detailed explanations. In this post, I explain the titular paper - One-shot Learning with Memory-Augmented Neural Networks.

原文链接:http://rylanschaeffer.github.io/content/research/one_shot_learning_with_memory_augmented_nn/main.html


3.【博客】Medical Image Analysis with Deep Learning 

简介:


Analyzing images and videos, and using them in various applications such as self driven cars, drones etc. with underlying deep learning techniques has been the new research frontier. The recent research papers such as “A Neural Algorithm of Artistic Style”, show how a styles can be transferred from an artist and applied to an image, to create a new image. Other papers such as “Generative Adversarial Networks” (GAN) and “Wasserstein GAN” have paved the path to develop models that can learn to create data that is similar to data that we give them. Thus opening up the world to semi-supervised learning and paving the path to a future of unsupervised learning.

原文链接:http://www.kdnuggets.com/2017/03/medical-image-analysis-deep-learning.html


4.【论文 & 代码】Conditional Similarity Networks

简介:


What makes images similar? To measure the similarity between images, they are typically embedded in a featurevector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.

原文链接:https://github.com/andreasveit/conditional-similarity-networks

论文链接:https://arxiv.org/pdf/1603.07810.pdf


5.【博客】Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis

简介:


We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution – but complete – output. To this end, we introduce a 3D-EncoderPredictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct finescale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data.

原文链接:http://graphics.stanford.edu/projects/cnncomplete/


本文转载自:http://www.jianshu.com/p/72cbdbf69928

共有 人打赏支持
AllenOR灵感
粉丝 10
博文 2634
码字总数 82983
作品 0
程序员
预测流行偏好,时尚 AI 未来可望取代造型师

【Technews科技新报】预测时尚潮流是一项需要天分的工作,还得仰赖一个庞大的系统让少数人追捧的时尚进入大众流行市场,进而让业者赚取大笔钞票。现在预测工作也可以交给人工智能,让服饰业者...

黄 嬿
2017/12/26
0
0
MySQL XtraBackup 备份

备注:先安装XtraBackup 下载地址:https://www.percona.com/downloads/XtraBackup/LATEST/ 此处我的版本是:percona-xtrabackup-24-2.4.6-2.el7.x8664.rpm 本地安装: yum localinstall per......

Lydialyd
06/26
0
0
MySQL XtraBackup安装备份

备注:先安装XtraBackup 下载地址:https://www.percona.com/downloads/XtraBackup/LATEST/ 此处我的版本是:percona-xtrabackup-24-2.4.6-2.el7.x8664.rpm 本地安装: yum localinstall per......

Lydialyd
06/26
0
0
人工智能知识整理-第1辑(20170603)-机器学习入门资源汇总

有一天我忽然忘记了一个函数的用法,于是就上谷歌搜,结果搜出来的竟然是自己写的一篇笔记,上面有很详细的回答。当时感觉是跟另外一个自己进行交流,那一个是刚学完知识,印象还非常深的自己...

人工智豪
2017/06/03
0
0
Aruba AC如何通过CLI备份及导入导出

通过CLI登录Aruba AC。Aruba AC是通过SSH加密的,因此建议使用PUTTY或者Xshell等支持SSH的终端工具。导入导出需要使用到tftp server,请先与本地建立tftp server. 1.配置文档备份 备份之前,先...

Fonphxion
2017/04/18
0
0

没有更多内容

加载失败,请刷新页面

加载更多

下一页

arts-week5

Algorithm 824. Goat Latin - LeetCode 152. Maximum Product Subarray - LeetCode 110. Balanced Binary Tree - LeetCode 67. Two Sum II - Input array is sorted - LeetCode 665. Non-dec......

yysue
12分钟前
0
0
iOS开发之AddressBook框架详解

iOS开发之AddressBook框架详解 一、写在前面 首先,AddressBook框架是一个已经过时的框架,iOS9之后官方提供了Contacts框架来进行用户通讯录相关操作。尽管如此,AddressBook框架依然是一个非...

珲少
41分钟前
1
0
两年摸爬滚打 Spring Boot,总结了这 16 条最佳实践

Spring Boot是最流行的用于开发微服务的Java框架。在本文中,我将与你分享自2016年以来我在专业开发中使用Spring Boot所采用的最佳实践。这些内容是基于我的个人经验和一些熟知的Spring Boot...

Java填坑之路
今天
3
0
《Spring5学习》04 - 面向切面编程

一、Spring面向切面编程的基本概念 面向切面编程(即AOP):把项目中需要再多处使用的功能比如日志、安全和事务等集中到一个类中处理,而不用在每个需要用到该功能的地方显式调用。 横切关注...

老韭菜
今天
2
0
day61-20180819-流利阅读笔记

跑道没了,它们还在跑:澳门赛狗业的遗孤 Daniel 2018-08-19 1.今日导读 相信你早就知道香港有个赛马会,可是你听说过香港的邻居澳门原本有个赛狗会吗?其实,对于澳门人来说,赛狗这项活动历...

aibinxiao
今天
15
0

没有更多内容

加载失败,请刷新页面

加载更多

下一页

返回顶部
顶部