Active　learning　(AL)　tries　to　maximize　the　model＇s　performance　when　the　labeled　data　set　is　limited,　and　the　annotation　cost　is　high.　Although　it　can　be　efficiently　implemented　in　deep　neural　networks　(DNNs),　it　is　questionable　whether　the　model　can　maintain　the　ability　to　generalize　well　when　there　are　significant　distributional　deviations　between　the　labeled　and　unlabeled　data　sets.　In　this　article,　we　consider　introducing　adversarial　training　and　adversarial　samples　into　AL　to　mitigate　the　problem　of　degraded　generalization　performance　due　to　different　data　distributions.　In　particular,　our　proposed　adversarial　training　AL　(ATAL)　has　two　advantages,　one　is　that　adversarial　training　by　different　networks　enables　the　network　to　have　better　prediction　performance　and　robustness　with　limited　labeled　samples.　The　other　is　that　the　adversarial　samples　generated　by　the　adversarial　training　can　effectively　expand　the　labeled　data　set　so　that　the　designed　query　function　can　efficiently　select　the　most　informative　unlabeled　samples　based　on　the　expanded　labeled　data　set.　Extensive　experiments　have　been　performed　to　verify　the　feasibility　and　efficiency　of　our　proposed　method,　i.e.,　CIFAR-10　demonstrates　the　effectiveness　of　our　method　-　new　state-of-the-art　robustness　and　accuracy　are　achieved.　©　2014　IEEE.