Transferlearningisamachinelearningtechniquewhereamodeldevelopedforataskisreusedasthestartingpointforamodelonasecondtask.Ithasgainedsignificantattentioninrecentyearsduetoitsabilitytoleverageknowledgefromrelateddomains,reducingtheneedforlargeamountsoflabeleddataandimprovingmodelperformance."ASurveyonTransferLearning"providesacomprehensiveoverviewofthefield,coveringkeyconcepts,methodologies,andapplications.Thesurveydiscussesdifferenttransferlearningapproaches,suchasinductive,transductive,andunsupervisedtransferlearning,alongwithpopulartechniqueslikefeature-basedandinstance-basedtransfer.Italsoexploreschallenges,suchasnegativetransferanddomainadaptation,whilehighlightingreal-worldapplicationsinareaslikecomputervision,naturallanguageprocessing,andhealthcare.Thissurveyservesasavaluableresourceforresearchersandpractitionerslookingtounderstandthestate-of-the-artintransferlearning,itslimitations,andfutureresearchdirections.
