Self-knowledge　distillation　does　not　require　a　pre-trained　teacher　network　like　traditional　knowledge　distillation.　Existing　methods　either　require　additional　parameters　or　require　additional　memory　consumption.　To　alleviate　this　problem,　this　paper　proposes　a　more　efficient　self-knowledge　distillation　method,　named　LRMS　(learning　from　role-model　samples).　In　every　mini-batch,　LRMS　selects　out　a　role-model　sample　for　each　sampled　category,　and　takes　its　prediction　as　the　proxy　semantic　for　the　corresponding　category.　Then,　predictions　of　the　other　samples　are　constrained　to　be　consistent　with　the　proxy　semantics,　which　makes　the　distribution　of　predictions　for　samples　within　the　same　category　more　compact.　Meanwhile,　the　regularization　targets　corresponding　to　proxy　semantics　are　set　with　a　higher　distillation　temperature　to　better　utilize　the　classificatory　information　about　the　categories.　Experimental　results　show　that　diverse　architectures　achieve　improvements　on　four　image　classification　datasets　by　using　LRMS.　Code　is　acaliable:　https://github.com/KAI1179/LRMS　　©　2024　IEEE.