资源描述:
《Very Deep Convolutional Networks for Large-Scale Image Recognition.pdf》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、VeryDeepConvolutionalNetworksforLarge-ScaleImageRecognitionKarenSimonyanAndrewZissermanVisualGeometryGroup,UniversityofOxford{karen,az}@robots.ox.ac.ukAbstractInthisworkweinvestigatetheeffectoftheconvolutionalnetworkdepthonitsaccuracyinthelarge-scaleimagerecogni
2、tionsetting.Ourmaincontributionisathoroughevaluationofnetworksofincreasingdepth,whichshowsthatasignifi-cantimprovementontheprior-artconfigurationscanbeachievedbypushingthedepthto16–19weightlayers.ThesefindingswerethebasisofourImageNetChallenge2014submission,whereou
3、rteamsecuredthefirstandthesecondplacesinthelocalisationandclassificationtracksrespectively.1IntroductionConvolutionalnetworks(ConvNets)haverecentlyenjoyedagreatsuccessinlarge-scalevisualrecognition[10,16,17,19]whichhasbecomepossibleduetothelargepublicimagereposito
4、ries,suchasImageNet[4],andhigh-performancecomputingsystems,suchasGPUsorlarge-scaledis-tributedclusters[3].Inparticular,animportantroleintheadvanceofdeepvisualrecognitionarchi-tectureshasbeenplayedbytheImageNetLarge-ScaleVisualRecognitionChallenge(ILSVRC)[1],whic
5、hhasservedasatestbedforafewgenerationsoflarge-scaleimageclassificationsystems,fromhigh-dimensionalshallowfeatureencodings[13](thewinnerofILSVRC-2011)todeepCon-vNets[10](thewinnerofILSVRC-2012).WithConvNetsbecomingmoreofacommodityinthecomputervisionfield,anumberofa
6、ttemptshavebeenmadetoimprovetheoriginalarchitectureof[10]inabidtoachievebetteraccuracy.Forinstance,thebest-performingsubmissionstotheILSVRC-2013[16,19]utilisedsmallerreceptivewindowsizeandsmallerstrideofthefirstconvolutionallayer.Anotherlineofimprovementsdealtwit
7、htrainingandtestingthenetworksdenselyoverthewholeimageandovermultiplescales[7,16].arXiv:1409.1556v2[cs.CV]15Sep2014Inthispaper,weaddressanotherimportantaspectofConvNetarchitecturedesign–itsdepth.Tothisend,wefixotherparametersofthearchitecture,andsteadilyincreaset
8、hedepthofthenetworkbyaddingmoreconvolutionallayers.Therestofthepaperisorganisedasfollows.InSect.2,wedescribeourConvNetconfigurations.Thedetailsoftheimageclassificationt