深度學(xué)習(xí)可復(fù)現(xiàn)性和可解釋人工智能（XAI）

DeepLearningReproducibilityandExplainableAI(XAI)

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Dr.Leventi-Peetz

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anastasia-maria.leventi-peetz@bsi.bund.de

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Abstract

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Abstract

ThenondeterminismofDeepLearning(DL)trainingalgorithmsanditsinfluenceontheexplainabilityofneuralnetwork(NN)modelsareinvestigatedinthisworkwiththehelpofimageclassificationexamples.Todiscusstheissue,twoconvolutionalneuralnetworks(CNN)havebeentrainedandtheirresultscompared.Thecomparisonservestheexplorationofthefeasibilityofcreatingdeterministic,robustDLmodelsanddeterministicexplainableartificialintelligence(XAI)inpractice.Successesandlimitationofallherecarriedouteffortsaredescribedindetail.Thesourcecodeoftheattaineddeterministicmodelshasbeenlistedinthiswork.Reproducibilityisindexedasadevelopment-phase-componentoftheModelGovernanceFramework,proposedbytheEUwithintheirexcellenceinAIapproach.Furthermore,reproducibilityisarequirementforestablishingcausalityfortheinterpretationofmodelresultsandbuildingoftrusttowardstheoverwhelmingexpansionofAIsystemsapplications.Problemsthathavetobesolvedonthewaytoreproducibilityandwaystodealwithsomeofthem,areexaminedinthiswork.

TableofContents

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TableofContents

Documenthistory 2

Abstract 3

Introduction 6

ReproducibleMLmodels

6

Factorshinderingtrainingreproducibility

6

Organizationandaimofthiswork

7

Grad-CAMNN-Explanations 9

NetworkarchitecturesandHW

9

InceptionV3

10

Soundnessandstabilityofexplanations

11

Xception

13

InceptionV3vs.Xception

15

Self-trainedModels 18

DeterministicConvNet

18

DeterministicminiXception

21

Conclusionsandfuturework 24

References 26

Introduction

6

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Introduction

ReproducibleMLmodels

ThereproducibilityofMLmodelsisasubjectofdebatewithmanyaspectsunderinvestigationbyresearchersandpractitionersinthefieldofAIalgorithmsandtheirapplications.Reproducibilityreferstotheabilitytoduplicatepriorresultsusingthesamemeansasusedintheoriginalwork,forexamplethesameprogramcodeandrawdata.However,MLexperienceswhatiscalledareproducibilitycrisisanditisdifficulttoreproduceimportantMLresults,somealsodescribedaskeyresults[

22

,

21

,

13

,

29

].Experiencereportsrefertomanypublicationsasbeingnotreplicable,orbeingstatisticallyinsignificant,orsufferingfromnarrativefallacy[

5

].EspeciallyDeepReinforcementLearninghasreceivedalotofattentionwithmanypapers[

5

,

27

,

25

,

14

]andblogposts[

24

]investigatingthehighvarianceofsomeresults.BecauseitisdifficulttodecidewhichMLresultsaretrustworthyandgeneralizetoreal-worldproblems,theimportanceofreproducibilityisgrowing.Acommonproblemconcerningreproducibilityiswhenthecodeisnotopen-sourced.Thereviewof400publicationsoftwotopAIconferencesinthelastyears,showedthatonly6%ofthemsharedtheusedcode,onethirdsharedthedataonwhichalgorithmsweretestedandhalfsharedpseudocode[

16

,

23

].Initiativeslikethe2019ICLRreproducibilitychallenge[

34

]andtheReproducibilityChallengeofNeurIPS2019[

38

,

35

],thatinvitemembersoftheAIcommunitytoreproducepapersacceptedattheconferenceandreportontheirfindingsviatheOpenReviewplatform(

/group?

id=NeurIPS.cc/2019/Reproducibility_Challenge

),demonstrateanincreasingintentiontomakemachinelearningtrustworthybymakingitcomputationallyreproducible[

19

].Reproducibilityisimportantformanyreasons:Forinstance,toquantifyprogressinML,ithastobecertainthatnotedmodelimprovementsoriginatefromtrueinnovationandarenotthesheerproductofuncontrolledrandomness[

5

].Alsofromthedevelopmentpointofview,adaptationsofmodelstochangingrequirementsandplatformsarehardlypossibleintheabsenceofbaselineorreferencecode,whichworksaccordingtoagreeduponexpectations.Thelattercouldgettransparentlyextendedorchangedbeforetestedtomeetnewdemands.ForMLmodels,itisthesonamedinferentialreproducibilitywhichisimportantasarequirementandstatesthatwhentheinferenceprocedureisrepeated,theresultsshouldbequalitativelysimilartothoseoftheoriginalprocedure[

13

].However,trainingreproducibilityisalsoanecessarysteptowardstheformationofasystematicframeworkforanend-to-endcomparisonofthequalityofMLmodels.ToourknowledgesuchaframeworkdoesnotyetexistanditshouldbeessentialifcriteriaandguaranteesregardingthequalityofMLmodelshavetobeprovided.Securityandsafetyconsiderationsareinevitablyinvolved:Forinstance,whenamodelexecutesapureclassificationexercise,decidingforexampleifatestimageshowsacatoradog,itisnotnecessarilycriticalwhenthemodel’sdecisionturnsouttobewrong.Ifhoweverthemodelisincorporatedintoaclinicaldecision-makingsystem,thathelpsmakepredictionsaboutpathologicconditionsonthebasisofpatients’data,orispartofanautomateddrivingsystem(ADS)whichactivelydecidesifavehiclehastoimmediatelystoporkeepspeeding,thenthedecisionhastobeverifiablycorrectandunderstandableateverystageofitsformation.TheincreasingdependencyonMLfordecisionmakingleadstoanincreasingconcernthattheintegrationofmodelswhichhavenotbeenfullyunderstoodcanleadtounintendedconsequences[

20

].

Factorshinderingtrainingreproducibility

Itiswellknownthatwhenamodelistrainedagainwiththesamedataitcanproducedifferentpredictions[

8

,

7

].Tothereasonsthatmakereproducibilitydifficulttherebelong:differentproblemformulations,missingcompatibilitybetweenDNN-architectures,missingappropriatebenchmarks,differentOS,differentnumericallibraries,systemarchitecturesorsoftwareenvironmentslikethePythonversionetc.Reproducibilityasabasisforthegenerationofsoundexplanationsandinterpretationsofmodeldecisionsisalsoessentialinviewoftheimmensecomputationaleffortandcostsinvolvedwhenapplyingoradaptingalgorithms,oftenwithoutspecificknowledgeaboutthehardware,theparameter-tuningandthe

Introduction

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energyconsumptiondemandedforthetrainingofamodel,whichattheendmightleadtoinconclusiveresults.Furthermore,itisalsodifficulttotrainmodelstoexpectedaccuracyevenwhentheprogramcodeandthetrainingdataareavailable.ChangesinTensorFlow,inGPUdrivers,orevenslightchangesinthedatasets,canhurtaccuracyinsubtleways[

46

,

45

].Inaddition,manyMLmodelsaretrainedonrestricteddatasets,forexamplethosecontainingsensitivepatientinformation,thatcan’tbemadepubliclyavailable[

1

].Whenprivacybarriersareimportantconsiderationsfordatasharing,socalledreplicationprocesseshavetobeused,toinvestigatetheextenttowhichtheoriginalmodelgeneralizestonewcontextsandnewdatapopulations,anddecidewhethersimilarconclusionstothoseoftheoriginalmodelcanbedelivered.However,thereexistalsocertainuniquechallengeswhichMLreproducibilityposes.ThetrainingofMLmodelsmakesuseofrandomness,especiallyforDL,usuallyemployingstochasticgradientdescent,regularizationtechniquesetc.[

3

].Randomizedproceduresresultindifferentfinalvaluesforthemodelparameterseverytimethecodeisexecuted.Onecansetallpossiblerandomseeds,howeveradditionalparameters,commonlynamedsilentparameters,associatedwithmoderndeeplearning,havebeenfoundtoalsohaveaprofoundinfluenceonbothmodelperformanceandreproducibility.High-levelframeworkslikeKerasarereportedtohidelow-levelimplementationdetailsandcomewithimplicithyperparameterchoicesalreadymadefortheuser.Alsohiddenbugsinthesourcecodecanleadtodifferentoutcomesindependenceoflinkedlibrariesanddifferentexecutionenvironments.Moreover,thecosttoreproducestate-of-the-artdeeplearningmodelsisoftenextremelyhigh.Innaturallanguageprocessing(NLP),transformersrequirehugeamountsofdataandcomputationalpowerandcanhaveinexcessof100billiontrainableparameters.Largeorganizationsproducemodels(likeOpenAI’sGPT-3)whichcancostmillionsofdollarsincomputingpowertotrain[

1

,

3

,

12

].Tofindthetransformerthatachievesthebestpredictiveperformanceforagivenapplication,meta-learnerstestthousandsofpossibleconfigurations.Thecosttoreproduceoneofthemanypossibletransformermodelshasbeenestimatedtorangefrom1millionto

3.2millionUSDwithusageofpubliclyavailablecloudcomputingresources[

39

,

3

].ThisprocessisestimatedtogenerateCO2emissionswithavolumewhichamountstothefivefoldofemissionsofanaveragecar,generatedoveritsentirelifetimeontheroad.Theenvironmentalimplicationsattachedtoreproducibilityendeavorsofthisrangearedefinitelyprohibitive[

3

].Aspossiblesolutiontothisproblem,therehasbeenproposedtheoptiontoletexpensivelargemodelsgetproducedonlyonce,whileadaptationsofthesemodelsforspecialapplicationsshouldbemadetransparentandreproduciblewiththeuseofmoremodestresources[

3

].

Organizationandaimofthiswork

ThemajorityofmethodsforexplainableAIareattributebased,theyhighlightthosedatafeatures(attributes),thatmostlycontributedtothemodel’spredictionordecision.Convolutionalneuralnetworks(CNN,orConvNet)arestate-of-the-artarchitectures,forwhichvisualexplanationscanbeproduced,forexamplewiththeGradient-weightedClassActivationMappingmethod(Grad-CAM)[

37

,

11

],whichisalsothemethodusedinthiswork.Inthesecondpartofthiswork,Grad-CAMexplanationsfortwopre-trainedandestablishedCNNmodels,whichuseTensorFlow,willbediscussedwithfocusonthedifferencesoftheirresults,whenthesametest-dataaregivenasinput.Itiswellknownthatwhendifferentexplainabilitymethodsareappliedonaneuralnetwork,differentresultsaretobeexpected.Thefactthatasingleexplainabilitymethod,whenappliedontwosimilarCNN-architectures,canproducedifferentresultsforthesametest-data,hasreceivedlessattentionintheliteraturebutisworthtoanalyzeinthereproducibilitycontext.Inthethirdpart,theownimplementation,trainingandresultsoftworelativelysimpleCNNmodelsarediscussed.DifferencesoftheGrad-CAM-explanationsforidenticalimagesclassifiedwiththesetwonetworksareanalyzed,withspecialfocusontheinfluenceofthecomputinginfrastructureonthemodelexecution.TheeffortstorenderthesetwomodelsdeterministicaredescribedinSection

3

indetail,againwithspecialfocusontheinfluenceofthecomputinginfrastructureontheresults.Successandlimitationsarenoted,thepartlyachieveddeterministiccodeislisted.ItisworthmentioningthatdifferentbehaviorsacrossversionsofTensorFlow,aswellasacrossdifferentcomputationalframeworksaredocumentedtobenormallyexpected.TensorFlowwarnsthatfloatingpointvaluescomputedbyops,maychangeatanytimeandusersshouldrelyonlyonapproximateaccuracyandnumericalstability,notonthe

Introduction

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specificbitscomputed.Therecouldbefoundnoexperiencereports,astohowachangeofspecificbitscouldinfluenceMLresults,forinstanceinworstcasebyalteringthenetwork’sclassificationoritsexplanation,orboth.AccordingtoTensorFlow,changestonumericalformulasinminorandpatchreleases,shouldresultincomparableorimprovedaccuracyofspecificformulas,withthecautionthatthismightdecreasetheaccuracyfortheoverallsystem.AlsomodelsimplementedinoneversionofTensorFlow,cannotrunwithnextsubversionsandversionsofTensorFlow.Thereforepublishedcodewhichwasonceprovedtowork,ispossiblynottouseagainwithinshorttimeafteritscreation.Torunmorethanonesubversionsonthesamesystem,whenusinggraphicHWsupport,wasnotpossible.ThisworkaimsatdrawingattentiontothechallengesthatadheretocreatingreproducibletrainingprocessesinDeepLearninganddemonstratespracticalstepstowardsreproducibility,discussingtheirpresentlimitations.InSection

4

conclusionsofthisworkandviewstowardsfutureinvestigationsinthesamedirectionarepresentedinasummary.Ithastobenotedthattheimpactofwhatiscalledunderspecification,wherebythesametrainingprocessesproducesmultiplemachine-learningmodelswhichdemonstratedifferencesintheirperformance,isoutofscopeofthiswork[

18

].

Grad-CAMNN-Explanations

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Grad-CAMNN-Explanations

NetworkarchitecturesandHW

Convolutionalneuralnetworks,originallydevelopedfortheanalysisandclassificationofobjectsindigitalimages,representthecoreofmoststate-of-the-artcomputervisionsolutionsforawidevarietyoftasks[

41

].AbriefbutcomprehensivehistoryofCNNcanbefoundinmanysources,forexamplein[

9

],wherebythetendencyhasalwaysbeentowardsmakingCNNincreasinglydeeper.DevelopmentsofthelastyearshaveledtotheInceptionarchitecture,whichincorporatesthesocalledInceptionmodules,thatexistalreadyinseveraldifferentversions.Anewarchitecture,whichinsteadofstacksofsimpleconvolutionalnetworks,containsstacksofconvolutionsitself,wasproposedbyFran?oisCholletwithhisExtremeInceptionorXceptionmodel.Xceptionwasprovedtobecapableoflearningricherrepresentationswithlessparameters[

9

].CholletdeliveredtheXceptionimprovementstotheInceptionfamilyofNN-architectures,byentirelyreplacingInceptionmoduleswithdepthwiseseparableconvolutions.Xceptionalsousesresidualconnections,placedinallflowsofthenetwork[

9

,

17

].Theroleofresidualswasobservedasespeciallyimportantfortheconvergenceofthenetwork[

44

],howeverCholletmoderatesthisimportance,becausenon-residualmodelshavebeenbenchmarkedwiththesameoptimizationconfigurationastheresidualones,whichleavesthepossibilityopen,thatanotherconfigurationmighthaveprovedthenon-residualversionbetter[

9

].Finally,thebuildingoftheimprovedXceptionmodelswasmadepossiblebecauseanefficientdepthwiseconvolutionimplementationbecameavailableinTensorFlow.TheXceptionarchitecturehasasimilarnumberofparametersasInceptionV3.ItsperformancehoweverhasbeenfoundtobebetterthanthatofInception,accordingtotestsontwolarge-scaleimageclassificationtasks[

9

].Forpracticaltestsinthiswork,InceptionV3andXceptionhavebeenchosenforresultscomparisons.ThetwonetworksarepretrainedonatrimmedlistoftheImageNetdataset,soastobeabletorecognizeonethousandnon-overlappingobjectclasses[

9

].

InceptionV3

Theexactdescriptionofthenetwork,itsparametersandperformancearegivenintheworkofChristianSzegedy[

42

].Thedescriptionofthetraininginfrastructurereferstoasystemof50replicas,(probablyidenticalsystems),runningeachonaNVidiaKeplerGPU,withbatchsize32,for100epochs.Thetimedurationofeachepochisnotgiven.

Xception

Chollethasused60NVIDIAK80GPUsforthetraining,whichtookadurationof3daystime.Thenumberofepochsisnotgiven.Thenetworkandtechnicaldetailsaboutthetrainingarelistedintheoriginalwork[

9

].

Xceptionhasasimilarnumberofparameters(ca.23million)asInceptionV3(ca.24million).TheHWexecutionenvironmentsemployedfortheheredescribedexperimentsarethefollowing:

HW-1:GPU:NVIDIATITANRTX:24GB(GDDR6),576NVIDIATuringmixed-precisionTensorCores,4608CUDACores.

HW-2:CPU:AMDEPYC7502P32-Core,SMT,2GHz(T:2.55GHz),RAM128GB.

HW-3:GPU:NVIDIAGeForceRTX2060:6GB(GDDR6),240NVIDIATuringmixed-precisionTensorCores,1920CUDACores.

HW-4:CPU:AMDRyzenThreadripper3970X32-Core,SMT,3.7GHz(T:4.5GHz),RAM256GB.

HW-5:CPU:AMDRyzen75800X8-Core,SMT,3.8GHz(T:4.7GHz),RAM64GB.

EachofthepretrainedmodelsisverifiedtodeliverthesameresultsforallhereconsideredCPUorGPUdifferentexecutionenvironments.Theclassificationsandtheaccordingnetworkexplanationsaredeterministicwhenperformedunderlaboratoryconditions,asalsoexpected.Plausibilityandstabilityissuesoftheexplanationswillbementionedparalleltothetests.

Grad-CAMNN-Explanations

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InceptionV3

Inthispartexamplesofpredictions,calculatedwiththeInceptionV3networkarediscussed.InFig.

1

(a)and

(b)respectively,therearedepictedactivationheatmapswhichhavebeenproducedtoidentifythoseregionsoftheimagechow-cat,thatcorrespondtothedog(“chow”)andthecat(“tabby”)respectively.IdenticalrespectiveaccuracieshavebeencalculatedforeachclassificationindependentoftheemployedHW,aswasverifiedbythetestsperformedwithallHW-environmentslistedattheendofsection

2.1

.The“chow”hasbeenpredictedwith30%probabilityandstandsinthefirstplaceonthetop-predictions-list,whilethecatgetsthethirdpositionwithaprobabilityof2.4%.

(a) (b)

Figure1:chow-cat:Grad-CAMexplanationsofInceptionV3fortheidentificationofthedog“chow”(a),inthefirstplaceonthetop-predictions-listandthecat“tabby”(b),inthethirdplaceonthetop-predictions-list.Thesecondplaceoccupiesa“Labradordog”.

InFig.

2

,heatmapsproducedbytheidentificationofthe“cockerspaniel”(a),the“toypoodle”(b),andthe“Persiancat”(c)respectively,havebeendemonstratedfortheimagespaniel-kitty.

(a) (b) (c)

Figure2:spaniel-kitty:Grad-CAMexplanationofInceptionV3fortheidentificationofthe“cockerspaniel”(a),the“toypoodle”(b)andthe“Persiancat”(c),see

Table1

.

Grad-CAMNN-Explanations

Class

HW-2

HW-1

1 cockerspaniel

0.56762594

0.56761914

2 toypoodle

0.08013367

0.08014054

3 clumber

0.02106595

0.02107035

4 DandieDinmont

0.01964365

0.01964012

5 Pekinese

0.01867950

0.01868443

6 miniaturepoodle

0.01846011

0.01846663

7 Blenheimspaniel

0.01425239

0.01424699

8 Maltesedog

0.01124849

0.01124578

9 Chihuahua

0.01103328

0.01103479

10 Norwichterrier

0.00741338

0.00741514

11 Sussexspaniel

0.00703137

0.00703068

12 Yorkshireterrier

0.00689254

0.00689154

13 Norfolkterrier

0.00662250

0.00662296

14 Lhasa

0.00609926

0.00609862

15 Pomeranian

0.00608485

0.00608792

16 Persiancat

0.00489533

0.00489470

17 goldenretriever

0.00428663

0.00428840

Table1:InceptionV3:ClassificationProbabilitiesfortheimagespaniel-kitty,seeFig.

2

.

In

Table1

therearelistedthescoresofthefirst17classesonthetop-predictions-list,ascalculatedintwoHWexecutions(HW-1,HW-2).Thepredictionscoresarealmostidentical,asisobviousbycomparingthecolumnsin

Table1

,whileinthefewcases,whenslightdifferencesexistintheprobabilityvalues,thesedifferencesappearonlyafterthefourthdecimalplace.The“cockerspaniel”isthetoppredictionandrepresentsactuallythecorrectclassificationofthedograce,predictedwithaprobabilityofalmost57%,whilethe“Persiancat”inplace16ofthelist,whichisalsoacorrectprediction,hasaprobabilityofapproximately0.5%.The“toypoodle”with8.0%probabilitystandsinthesecondplaceonthelist,whiletherestoflistplaces,downtoplacesixteenofthe“Persiancat”,arealloccupiedbydograces(see

Table1

).

Soundnessandstabilityofexplanations

Acarefulobservationofthedeliverednetworkexplanationsshowsthattheyarepartlyarbitraryandhardlyintuitive,andthisindependentlyofawrong,orrightclassprediction.Forexample,thenetworkreasoningbehindthe“toypoodle”classificationinFig.

2

(b),whichiswrongasfarastheraceofthedogisconcerned,butrightasfarastheanimalcategoryidentified(adog),cannotbenotedassound.Themainreasonisbecausethemostactivated,andthereforethemostrelevanttothetargetidentificationregion(markedred),pointstoapartoftheimagethatliesinemptyspace,beyondthecontourofthetarget.Themarkedredregionliesclosetowhatonecoulddescribeasagenericfeature,thepaws,whichiscommontoavarietyofanimals.Atoogenericfeatureofferslittleconfidenceinbeingagoodexplanation,ifassumedthatitisonlytheaccuracyofthefeature’slocalizationintheimagethatfails.Besides,thealgorithmcouldhavefocusedonthevicinityofthepawsoutofreasonsnotdirectlyassociatedwiththerecognitionofthe“poodle”.Observingthattheexplanationfortheidentificationofthe“Persiancat”,seeFig.

2

(c),highlightsthesamepaws,makestheunambiguityordefinitenessoftheexplanationsquestionable.Importantisalsotheinvestigationofthestabilityandconsistencyofthenetwork’sexplanations,astheyrelatetothereproducibilityofthenetworktoo.Forexample,itwouldbeexpectedthatanetworkwhichconcentratedonthedog’sheadtoexplainthefirstplaceofthetop-predictions-list,the“cockerspaniel”inFig.

2

(a),wouldprobablyalsopicktheheadtomainlyidentifythesecondmostprobableclassificationonthelist,whichthe“toypoodle”,seeninFig.

2

(b).Thisishowevernotthecase,whichmakestheconsistencybehindthelogicofexplanationsdoubtful.Obviously,thecat’sheadalsoreceiveshardlyanyattentionfortheexplanationoftherecognitionofthecatinFig.

2

(c).Itisnotpossibletoidentifysomecertainstrategywhichthenetwork

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Grad-CAMNN-Explanations

consistentlyemploysinordertoexplainclassifications,inthiscaseofanimals.Forfurtherinvestigations,asmallpartoftheimagespaniel-kitty,namelythepartcontainingthepaws,hasbeenremovedfromtheimageandthetop-predictions-listhasbeencalculatedagain.Withthenewtestimage,spaniel-kitty-paws-cutasinput,the“cockerspaniel”keepsthefirstplaceonthetop-predictions-list,see

Table2

,howeverthe“Persiancat”climbesnowfromplace16toplace2withaclassificationprobabilityrisingfrom0.5%to30%,whilethe“toypoodle”fallsdowntotheplace4ofthelist.

Class

HW-2

HW-1

1 cockerspaniel

0.43387938

0.43393657

2 Persiancat

0.03001592

0.03000891

3 Pekinese

0.02654952

0.02654130

4 toypoodle

0.01810920

0.01810851

5 DandieDinmont

0.01457902

0.01457707

6 Sussexspaniel

0.01415453

0.01415372

7 Goldenretriever

0.01363987

0.01363916

8 Miniaturepoodle

0.01088122

0.01088199

Table2:InceptionV3:ClassificationProbabilitiesfortheimagespaniel-kitty-paws-cut.

In

Table2

,thenewtop-fourpredictedclassesandtheirnewscoresaredisplayed.Therearenogreatchangesintheexplanationconcerningthe“cockerspaniel”forthemodifiedimage,theheadbeingtheparthighlightedagain.Howeverthevisualexplanationsfortheidentificationofthe“toypoodle”andthe“cat”havechangedconsiderably,asinFig.

3

tosee.

(a) (b) (c)

Figure3:spaniel-kitty-paws-cut:Grad-CAMexplanationofInceptionV3fortheidentificationofthe“cockerspaniel”(a),the“toypoodle”(b)andthe“Persiancat”(c),whenthepawsareremovedfromtheimage(compareresultsofFig.

2

).

The“toypoodle”isnowoverlayedbyadoubleheatspot,aminoroneattheendofthecat’sbodyandthemainonetotherightofthecat’shead,bothlyingoutsidethecontouroftherecognized“poodle”,seeFig.

3

(b).Althoughinthiscasetheclassificationiscorrect,theexplanationdoesn’tmakesenseatall,becausetheactivationregionliesentirelyoutsidethetarget(“toypoodle”).Onecouldarguethatatleasttheexplanationforthe“Persiancat”inFig.

3

(c)hasbeenimproved,incomparisontotheunchangedimage.Thehotactivationregionapproachesnowthecat’sheadinsteadofthepawswhichismorecharacteristicofthetarget.However,aconsiderablepartoftheclassactivationmapping(markedred),stillliesbeyondthecontourofthecatandtherefore,atleastthepositionoftherecognizedtarget,canbedescribedasnotaccurateorevenwrong.InceptionV3deliversidenticalresults,withrespecttochangingexecutionenvironments,thereforetheexplanationsandclassificationsofthenetworkareprovedtobedeterministic

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Grad-CAMNN-Explanations

underlaboratoryconditions,thatiswhennointentionalorunintentionalperturbationsareinsertedtothetestdata.

Xception

Inanalogyto

2.2

,objectdetectionsandtheirexplanationscalculatedwiththeXceptionnetworkareherediscussed.InFig.

4

(a)and(b)therearepresentedtheactivationheatmaps,producedbythenetworkfortheidentificationoftheimageregionsthatcorrespondtothe“dog”(“chow”),andthe“cat”respectively,(hereidentifiedas“Egyptiancat”,whereasInceptionV3identifiedthecatasa“Tabbycat”,compareFig.

1

).

(a)“chow” (b)“Egyptian_cat”

Figure4:chow-cat:Grad-CAMexplanationsofXceptionfortheidentificationofthe“chow”(a),inthefirstplaceofthetop-predictions-listandthe“Egyptiancat”(b),inthesecondplace.Thirdonthelististhe“tigercat”andfourththe“tabbycat”.Foracomparison,theorderofexplanationsgeneratedbyInceptionV3isgiveninthecaptionofFig.

1

.

InFig.

5

theactivationmapscorrespondingtotheidentificationofthe“cockerspaniel”,the“Frenchbulldog”,the“toypoodle”andthe“Persiancat”respectivelyaredemonstrated.SimilarlytotheInceptionV3case,describedintheprevioussection,allpredictionscoresarealmostidenticalbetweenallHWenvironmentexecutions.

Grad-CAM