2012-ImageNet Classification with Deep Convolutional Neural Networks

2012-ImageNet Classification with Deep Convolutional Neural Networks

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時間：2019-07-29

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1、ImageNetClassi?cationwithDeepConvolutionalNeuralNetworksAlexKrizhevskyIlyaSutskeverGeoffreyE.HintonUniversityofTorontoUniversityofTorontoUniversityofTorontokriz@cs.utoronto.cailya@cs.utoronto.cahinton@cs.utoronto.caAbstractWetrainedalarge,deepconvolutionalneuralnetworktocla

2、ssifythe1.2millionhigh-resolutionimagesintheImageNetLSVRC-2010contestintothe1000dif-ferentclasses.Onthetestdata,weachievedtop-1andtop-5errorratesof37.5%and17.0%whichisconsiderablybetterthanthepreviousstate-of-the-art.Theneuralnetwork,whichhas60millionparametersand650,000neu

3、rons,consistsof?veconvolutionallayers,someofwhicharefollowedbymax-poolinglayers,andthreefully-connectedlayerswitha?nal1000-waysoftmax.Tomaketrain-ingfaster,weusednon-saturatingneuronsandaveryef?cientGPUimplemen-tationoftheconvolutionoperation.Toreduceover?ttinginthefully-co

4、nnectedlayersweemployedarecently-developedregularizationmethodcalled“dropout”thatprovedtobeveryeffective.WealsoenteredavariantofthismodelintheILSVRC-2012competitionandachievedawinningtop-5testerrorrateof15.3%,comparedto26.2%achievedbythesecond-bestentry.1IntroductionCurrent

5、approachestoobjectrecognitionmakeessentialuseofmachinelearningmethods.Toim-provetheirperformance,wecancollectlargerdatasets,learnmorepowerfulmodels,andusebet-tertechniquesforpreventingover?tting.Untilrecently,datasetsoflabeledimageswererelativelysmall—ontheorderoftensofthou

6、sandsofimages(e.g.,NORB[16],Caltech-101/256[8,9],andCIFAR-10/100[12]).Simplerecognitiontaskscanbesolvedquitewellwithdatasetsofthissize,especiallyiftheyareaugmentedwithlabel-preservingtransformations.Forexample,thecurrent-besterrorrateontheMNISTdigit-recognitiontask(<0.3%)ap

7、proacheshumanperformance[4].Butobjectsinrealisticsettingsexhibitconsiderablevariability,sotolearntorecognizethemitisnecessarytousemuchlargertrainingsets.Andindeed,theshortcomingsofsmallimagedatasetshavebeenwidelyrecognized(e.g.,Pintoetal.[21]),butithasonlyrecentlybecomeposs

8、ibletocol-lectlabeleddatasetswithmillionsofimages.ThenewlargerdatasetsincludeLabel

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