Attribution　methods　explaining　a　particular　decision　for　a　given　convolutional　neural　network　(CNN)　have　gained　a　lot　of　attention　over　the　last　few　years.　Among　them,　approximation　methods　of　Shapley　values　are　considered　to　be　better　ways　of　assigning　attribution　scores　such　that　several　desirable　axioms　are　satisfied.　Nevertheless,　these　attribution　scores　may　still　be　misleading　or　inaccurate　due　to　the　inappropriate　selection　of　a　baseline　which　is　necessary　to　apply　Shapley　values　to　CNNs.　Previous　baseline　studies　have　focused　on　developing　a　ge-neric　baseline　selection　method　for　all　approximation　methods;　however,　we　find　that　designing　a　baseline　under　the　essence　of　the　selected　approximation　method　itself　produces　better　results　than　generic　ones.　With　this　ob-servation,　we　propose　two　primal　baseline　properties　for　Aumann-Shapley-based　attributions　and　design　a　gen-eral　objective　function　of　generating　a　baseline　iteratively　by　gradient　descent.　To　increase　efficiency,　we　further　reduce　the　objective　function　into　a　quadratic　optimization　problem　where　the　gradients　only　need　to　be　calcu-lated　once.　We　show　that　our　method　produces　better　attribution　results　than　several　state-of-the-art　baseline　selections　and　attribution　methods　on　both　qualitative　and　quantitative　experiments.(c)　2022　Elsevier　B.V.　All　rights　reserved.