الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Most modernsystemsutilizeartificialintelligenceandmachine
learning, especiallydeeplearningmodelswhichhavegainedan
increasedpopularityinthelastdecade.Theutilizationofthese
models hasbeenmoreplausibleduetothecontinuousprogress
in hardwareinnovations.
This canbeseeninthewidespreadoftouchscreendevices
that facilitatedthehumancomputerinteractionandincreased
the useofhandgesturesanddrawings.Asaresult,image-
relatedapplicationshavegainedaremarkableattention.Fur-
thermore,theemploymentofconvolutionalneuralnetworksin
image-relatedapplicationshaveshownanexceptionalperfor-
mance.
Alongside theprevalenceofmachinelearningmodels,con-
cerns regardingthosemodelshavebeenontherise.Theneedto
understand howsomemodels,likedeeplearningmodels,oper-
ate hasincreased.Hence,aresearchfielddedicatedtotheinter-
pretationofsuchmodelshasemerged.Inthiskindofresearch,
the aimistoexplaintheunderlyingworkflowofanexistingma-
chine learningmodel,ratherthantodevelopanewone.
v
In thisthesis,thesketchrecognitionproblemisusedasa
testbed forinvestigatingsomeofthelearningbehaviorsofcon-
volutional neuralnetworks.Ontheonehand,thenatureof
sketches ischallengingowingtotheirabstractandinconsistent
nature.Theexplanationofimage-relatedmodelsismorein-
formative andappealingduetotherichnatureofimagesover
sketches incolors,textures,backgroundscenery,etc.Onthe
other hand,thehumanfactorinsketch-relatedapplicationscan
be usedtoimprovetheresultsobtainedusingasketchrecogni-
tion model.Someofthehumantraitsinthesketchrecognition
task isexperimentedandusedasareferenceinexplainingthe
learning behaviorofconvolutionalneuralnetworks.Oneofthe
main propertiesaddressedinthisthesisistheabilityofanartifi-
cial networktodetectvisualanalogies(i.e.,similarities)among
differentobjects.
This thesisisorganizedintosevenchapters.Inwhatfollows,
the contentsofeachisbrieflyoutlined.
Chapter 1 presentsanintroductiontothethesisincluding
the motivationandthemaingoalsofthethesis.Thischapter
also givesadetaileddescriptionoftheorganizationofthethesis’
chapters.
vi
In Chapters 2 and 3, themainaspectsofartificialneuralnet-
works areintroduced.Architecturalvariationslikeconvolutional
and recurrentneuralnetworksarealsodiscussed.
Chapter 4 introducestheconceptofinterpretablemachine
learning models.Itshedslightonsomeoftherelatedworkin
the literature.
Chapter 5 outlines thesketchrecognitionproblem.Thischap-
ter highlightssomeoftherelatedrepresentationandrecognition
approaches.
Chapter 6 presentsanexperimentalapproachtostudythe
learning behaviorofconvolutionalneuralnetworksusingthe
sketch recognitionproblem.Theresultsobtainedfromthecon-
ducted experimentsalongwiththeiranalysesarediscussed.The
methodology andresultspresentedinthischapterispublished
in [6].
Finally inChapter 7, themainideaspresentedinthethesis
aresummarized.Thischaptergivestheconclusionandmain
resultsof[6]. Moreover,thesuggestionsforresearchpointsto
be conductedandattempingtosolveinthefutureareprovided.
Appendix A providespartsoftheprogramthatwasimple-
mented usingPython.