Thecollapseofdeepandnarrowneuralnetworksreferstothephenomenonwheresucharchitecturesfailtolearneffectivelyorgeneralizewellcomparedtowiderordifferentlystructurednetworks.Deepandnarrownetworks,characterizedbymanylayersbutfewneuronsperlayer,oftensufferfromoptimizationchallengeslikevanishinggradients,poorfeaturepropagation,andlimitedrepresentationalcapacity.Thiscanleadtodegradedperformance,traininginstability,orcompletefailuretoconverge.Researchsuggeststhatwidernetworksoralternativearchitectures(e.g.,residualconnections)oftenmitigatetheseissues.Thecollapsehighlightstheimportanceofbalancingdepthandwidthinneuralnetworkdesignforoptimallearningdynamics.
