09 2014 档案

ILSVRC2014检测总结
摘要：ILSVRC 2014结束一段时间了。从下面的表格来看，基本都是RCNN的路子，但是这些牛队都做了改进。自己和人家比差的太远啊，努力。teamresultsSpotlights and improveGoogLeNet0.439329(6 m)0.38(1m)Rcnn1. Increase size... 阅读全文
posted @ 2014-09-28 14:22 deeplearner_allen 阅读(2335) 评论(0) 推荐(0) 编辑
MVAPICH
摘要：from:https://developer.nvidia.com/mvapichMVAPICH2 is an open source implementation of Message Passing Interface (MPI) that delivers the best performan... 阅读全文
posted @ 2014-09-24 09:58 deeplearner_allen 阅读(934) 评论(0) 推荐(0) 编辑
parameter server
摘要：http://zeromq.orgZeroMQ\zero-em-queue\, \ØMQ\: Ø Connect your code in any language, on any platform. Ø Carries messages across inproc, IPC, TCP, TPI... 阅读全文
posted @ 2014-09-19 21:56 deeplearner_allen 阅读(507) 评论(0) 推荐(0) 编辑
svm
摘要：想用SVM代替softmax,损失函数有了，可是最后一层没有实现啊。好吧，今天得到启发，我觉得我要笨死了……https://github.com/s9xie/DSN/tree/experiment-no-polishhttp://vcl.ucsd.edu/~sxie/2014/09/12/dsn-p... 阅读全文
posted @ 2014-09-18 17:55 deeplearner_allen 阅读(255) 评论(0) 推荐(0) 编辑
How good are detection proposals, really?
摘要：How good are detection proposals, really?J. Hosang, R. Benenson,B. SchieleOral at BMVC 2014http://rodrigob.github.io/https://www.mpi-inf.mpg.de/depart... 阅读全文
posted @ 2014-09-17 15:56 deeplearner_allen 阅读(972) 评论(2) 推荐(0) 编辑
Very Deep Convolutional Networks for Large-Scale Image Recognition
摘要：Very Deep Convolutional Networks for Large-Scale Image RecognitionKaren Simonyan,Andrew ZissermanIn this work we investigate the effect of the convolu... 阅读全文
posted @ 2014-09-17 14:04 deeplearner_allen 阅读(1113) 评论(0) 推荐(0) 编辑
Object Detection with Discriminatively Trained Part Based Models
摘要：P. Felzenszwalb, R. Girshick, D. McAllester, D. RamananObject Detection with Discriminatively Trained Part Based ModelsIEEE Transactions on Pattern An... 阅读全文
posted @ 2014-09-17 10:05 deeplearner_allen 阅读(793) 评论(0) 推荐(0) 编辑
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
摘要：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionKaimingHe, Xiangyu Zhang, Shaoqing Ren, and Jian SunThe13th European Conf... 阅读全文
posted @ 2014-09-16 15:45 deeplearner_allen 阅读(5627) 评论(2) 推荐(0) 编辑
logistic regression svm hinge loss
摘要：二类分类器svm 的loss function 是 hinge loss：L(y)=max(0,1-t*y),t=+1 or -1,是标签属性. 对线性svm，y=w*x+b，其中w为权重，b为偏置项，在实际优化中，w,b是待优化的未知，通过优化损失函数，使得loss function最小，得到优化... 阅读全文
posted @ 2014-09-15 12:47 deeplearner_allen 阅读(801) 评论(0) 推荐(0) 编辑

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