[Thu, 9 Jul 2015 ~ Tue, 14 Jul 2015] Deep Learning in arxiv

这一期的神作论文有蛮多的，都非常有意思。

Feature Representation In ConvolutionalNeural Networks

该论文中论述了在某种CNN结构下，是否有准确率较高的off model的分类方法（这里是指非softmax）能达到更有效的分类结果呢？

论文给出了肯定的答案。

该论文还给出了各层特征重要性的图表，蛮有意思的

该论文还交代了实验中用到的开源代码。

Towards Good Practices for Very DeepTwo-Stream ConvNets

Openmpi for multi-gpu

Code: https://github.com/yjxiong/caffe/tree/action_recog

Two-Stream Convolutional Networks forAction Recognition in Videos

其中单个stream的准确率如下：

Combine的结果如下：

Understanding Intra-Class Knowledge InsideCNN

该论文利用可视化技术表述了cnn是如何进行类内区分的

另外在该文中也有引用到利用可视化表述cnn如何进行类间区分的。

是很好的一篇cnn可视化理解文章

COMPRESSING DEEP CONVOLUTIONAL NETWORKSUSING VECTOR QUANTIZATION

该论文描述了手机级别的模型存储压缩，非常好的一篇论文。

其实模型参数的压缩不仅会起到存储空间压缩（PQ）的作用，还会起到加速模型的作用例如SVD。

Unconstrained Facial Landmark Localizationwith Backbone-Branches Fully-Convolutional Networks

论文提出了Backbone-Branches Fully-Convolutional Neural Network (BB-FCN)的网络结构，非常有趣，论文中还给出了很多商业非商业的方法做比较，是一篇很好的人脸定位切入文章。

其中的网络结构如下：

结果对比表格：

Facial Landmark Detection by DeepMulti-task Learning

多任务学习可以提高某单一任务的精度。

时间： 2024-10-08 14:18:56

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