<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[sunshine_010的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/sunshine_010</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; sunshine_010]]></copyright><item><title><![CDATA[论文阅读笔记（五十六）：Image Super-Resolution Using Deep Convolutional Networks]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80368040</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80368040</guid><author>sunshine_010</author><pubDate>Fri, 18 May 2018 20:12:58 +0800</pubDate><description><![CDATA[Abstract—We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented a...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（五十五）：Self-Normalizing Neural Networks]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80367967</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80367967</guid><author>sunshine_010</author><pubDate>Fri, 18 May 2018 18:07:56 +0800</pubDate><description><![CDATA[Abstract 
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learni...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80316936</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80316936</guid><author>sunshine_010</author><pubDate>Mon, 14 May 2018 23:52:45 +0800</pubDate><description><![CDATA[Abstract. Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches ...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（五十三）：Understanding Deep Convolutional Networks]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80311112</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80311112</guid><author>sunshine_010</author><pubDate>Mon, 14 May 2018 16:19:00 +0800</pubDate><description><![CDATA[Abstract 
Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cas...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（五十二）：Outline Objects using Deep Reinforcement Learning]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80310562</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80310562</guid><author>sunshine_010</author><pubDate>Mon, 14 May 2018 16:08:55 +0800</pubDate><description><![CDATA[Abstract. Image segmentation needs both local boundary position information and global object context information. The performance of the recent state-of-the-art method, fully convolutional networks, ...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（五十一）：Understanding Deep Image Representations by Inverting Them]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80310505</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80310505</guid><author>sunshine_010</author><pubDate>Mon, 14 May 2018 15:43:14 +0800</pubDate><description><![CDATA[Abstract 
Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understa...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十九）：3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Pr..]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80301880</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80301880</guid><author>sunshine_010</author><pubDate>Sun, 13 May 2018 19:46:18 +0800</pubDate><description><![CDATA[Abstract—We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十八）：3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80300653</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80300653</guid><author>sunshine_010</author><pubDate>Sun, 13 May 2018 17:39:18 +0800</pubDate><description><![CDATA[Abstract.  
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automat...]]></description><category></category></item><item><title><![CDATA[基于卷积神经网络的不良地质体识别与分类]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80293185</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80293185</guid><author>sunshine_010</author><pubDate>Sat, 12 May 2018 17:18:24 +0800</pubDate><description><![CDATA[在泛函分析中，卷积、旋积或摺积(英语：Convolution)是通过两个函数f 和g 生成第三个函数的一种数学算子，表征函数f 与g经过翻转和平移的重叠部分的面积。

如果将参加卷积的一个函数看作区间的指示函数，卷积还可以被看作是“滑动平均”的推广。

地震勘探中,在地表激发点激发的地震子波(seismic wavelet)向地下传播,当遇到地下波阻抗界面时,一部分能量就会作为反射地震波向上反射回...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十七）：Attention Is All You Need]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80292450</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80292450</guid><author>sunshine_010</author><pubDate>Sat, 12 May 2018 16:17:19 +0800</pubDate><description><![CDATA[Abstract 
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the e...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十六）：Generative Adversarial Nets]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80292304</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80292304</guid><author>sunshine_010</author><pubDate>Sat, 12 May 2018 16:06:30 +0800</pubDate><description><![CDATA[Abstract 
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution,...]]></description><category></category></item><item><title><![CDATA[CVPR 2018 论文分享会]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80292085</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80292085</guid><author>sunshine_010</author><pubDate>Sat, 12 May 2018 15:51:38 +0800</pubDate><description><![CDATA[Deep Learning

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

Abstract 
Global covariance pooling in convolutional neural networks has ach...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十五）：Deformable Convolutional Networks]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80219473</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80219473</guid><author>sunshine_010</author><pubDate>Mon, 07 May 2018 00:55:31 +0800</pubDate><description><![CDATA[Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modul...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十四）：Deconvolutional Networks]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80217575</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80217575</guid><author>sunshine_010</author><pubDate>Sun, 06 May 2018 20:59:28 +0800</pubDate><description><![CDATA[Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues ...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十三）：Adaptive Deconvolutional Networks for Mid and High Level Feature Learning]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80211181</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80211181</guid><author>sunshine_010</author><pubDate>Sun, 06 May 2018 01:24:45 +0800</pubDate><description><![CDATA[We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture ...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十二）：Visualizing and Understanding Convolutional Networks]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80210147</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80210147</guid><author>sunshine_010</author><pubDate>Sat, 05 May 2018 22:08:26 +0800</pubDate><description><![CDATA[Abstract. 
Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al. [18]. However there is no clear understanding...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十一）：U-Net: Convolutional Networks for Biomedical Image Segmentation]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80203914</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80203914</guid><author>sunshine_010</author><pubDate>Sat, 05 May 2018 11:36:20 +0800</pubDate><description><![CDATA[Abstract. There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on t...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80184509</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80184509</guid><author>sunshine_010</author><pubDate>Thu, 03 May 2018 18:36:49 +0800</pubDate><description><![CDATA[We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our fin...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（三十九）：3D Convolutional Neural Networks for Human Action Recognition]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80181220</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80181220</guid><author>sunshine_010</author><pubDate>Thu, 03 May 2018 15:48:22 +0800</pubDate><description><![CDATA[This model extracts features from both spatial and temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. The developed model ...]]></description><category></category></item><item><title><![CDATA[论文阅读笔记（三十八）：Dynamic Zoom-in Network for Fast Object Detection in Large Images]]></title><link>https://blog.youkuaiyun.com/sunshine_010/article/details/80100873</link><guid>https://blog.youkuaiyun.com/sunshine_010/article/details/80100873</guid><author>sunshine_010</author><pubDate>Thu, 26 Apr 2018 23:15:30 +0800</pubDate><description><![CDATA[We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images. Detect...]]></description><category></category></item></channel></rss>