Domain　adaptation　is　proposed　to　improve　the　recognition　performance　of　the　domain　shift　or　the　dataset　bias.　The　domain　shift　is　a　very　common　problem,　which　is　caused　by　diverse　factors,　such　as　data　capturing　angles,　illumination,　and　image　quality　existing　in　the　natural　scene　image　understanding.　Since　the　domain　shift　leads　to　the　feature　distribution　discrepancy,　some　solutions　have　been　proposed　to　alleviate　the　distribution　discrepancy　by　mapping　feature　spaces　between　source　and　target　domains,　so　as　to　ensure　the　transferable　features　can　be　learned　by　the　deep　networks　during　the　end-to-end　training　for　the　classification　tasks.　However,　it　is　still　a　big　challenge　to　address　the　domain　shift　when　the　distribution　spaces　are　not　clearly　separated.　Inspired　by　the　adversarial　idea,　we　propose　a　novel　unified　deep　neural　network　architecture　named　the　unsupervised　domain　adversarial　adaptation　deep　neural　network.　It　addresses　the　domain　adaptation　problem　by　learning　domain-invariant　features　through　mitigating　the　feature　discriminative　ability　in　the　domain　classification　task　alternatively　by　alleviating　the　feature　distribution　discrepancy　in　the　main　classification　task.　Therefore,　in　our　proposed　unified　deep　network,　we　integrate　two　main　modules.　One　is　an　auxiliary　task　module　for　the　domain　classifier,　which　is　trained　to　make　sure　the　learned　features　are　domain-invariant　under　the　adversarial　optimization　strategy　by　minimizing　the　domain　discriminative　ability.　The　other　is　the　module　at　task-specific　layers　to　enhance　the　learning　of　the　transferable　features　with　the　less　distribution　discrepancy　by　adding　multiple　maximum　mean　discrepancy　constraints　to　map　the　features　to　reproducing　kernel　Hilbert　spaces.　The　experimental　results　show　that　the　features　learned　by　our　proposed　unified　deep　neural　network　perform　better　than　the　features　learned　by　previous　cross-domain　neural　networks　on　classification　tasks.　Our　proposed　approach　achieves　the　state-of-the-art　performance　on　three　cross-domain　datasets:　Office-31　(different　capturing　angles,　illumination,　and　image　quality),　Office-Caltech　(modified　from　Office-31)　and　a　combined　cross-domain　digit　dataset,　including　MNIST,　USPS　and　SVHN　(different　style　digits　in　each　dataset).　(C)　2019　Elsevier　B.V.　All　rights　reserved.