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DeepLearninganditsApplicationConvolutionalneuralnetworksConvolutionalneuralnetworks19SJTUDeepLearningLecture.Dataaugmentation19SJTUDeepLearningLecture.ImageflippingColortransformationImagecroppingImageNetcompetition19SJTUDeepLearningLecture.ConvolutionalNeuralNetworks30721FullyConnected

Layer32x32x3image->stretchto3072x

110x

3072weightsactivationinput1101

number:theresultoftakingadotproductbetweenarowofWandthe

input(a3072-dimensionaldot

product)19SJTUDeepLearningLecture.32203ConvolutionLayer32x32x3image->preservespatial

structurewidthheight32depthSJTUDeepLearningLecture.ConvolutionLayer32x32x3

image5x5x3

filter32Convolvethefilterwiththe

imagei.e.“slideovertheimage

spatially,computingdot

products”32321SJTUDeepLearningLecture.ConvolutionLayer32x32x3

image5x5x3

filter32Convolvethefilterwiththe

imagei.e.“slideovertheimage

spatially,computingdot

products”323Filtersalwaysextendthefulldepthoftheinput

volume22SJTUDeepLearningLecture.32332x32x3

image5x5x3

filter321

number:theresultoftakingadotproductbetweenthefilterandasmall5x5x3chunkofthe

image(i.e.5*5*3=75-dimensionaldotproduct+

bias)ConvolutionLayer23SJTUDeepLearningLecture.32332x32x3

image5x5x3

filter32convolve(slide)over

allspatial

locationsactivation

map2412828ConvolutionLayerSJTUDeepLearningLecture.3232332x32x3

image5x5x3

filterconvolve(slide)over

allspatial

locationsactivation

maps12828considerasecond,green

filter25ConvolutionLayerSJTUDeepLearningLecture.323628Forexample,ifwehad65x5filters,we’llget6separateactivation

maps:activation

maps3228Convolution

LayerWestacktheseuptogeta“newimage”ofsize

28x28x6!26ConvolutionLayerSJTUDeepLearningLecture.ConvolutionLayerPreview:ConvNetisasequenceofConvolutionLayers,interspersedwithactivation

functions32323CONV,ReLUe.g.65x5x3filters28286CONV,ReLUe.g.

105x5x6filtersCONV,ReLU27….102424SJTUDeepLearningLecture.57

SJTUDeepLearningLecture.ConvolutionlayerPreview[ZeilerandFergus

2013]ConvolutionLayer28SJTUDeepLearningLecture.PreviewConvolutionLayer29SJTUDeepLearningLecture.example5x5

filters

(32in

total)Wecallthelayerconvolutionalbecauseitisrelatedto

convolutionoftwo

signals:elementwisemultiplication

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