In　response　to　the　threat　of　adversarial　examples,　adversarial　training　provides　an　attractive　option　for　improving　robustness　by　training　models　on　online-augmented　adversarial　examples.　However,　most　existing　adversarial　training　methods　focus　on　improving　the　model＇s　robust　accuracy　by　strengthening　the　adversarial　examples　but　neglecting　the　increasing　shift　between　natural　data　and　adversarial　examples,　leading　to　a　decrease　in　natural　accuracy.　To　maintain　the　trade-off　between　natural　and　robust　accuracy,　we　alleviate　the　shift　from　the　perspective　of　feature　adaption　and　propose　a　Feature　Adaptive　Adversarial　Training　(FAAT)　optimizing　the　class-conditional　feature　adaption　across　natural　data　and　adversarial　examples.　Specifically,　we　propose　to　incorporate　a　class-conditional　discriminator　to　encourage　the　features　to　become　(1)　class-discriminative　and　(2)　invariant　to　the　change　of　adversarial　attacks.　The　novel　FAAT　framework　enables　the　trade-off　between　natural　and　robust　accuracy　by　generating　features　with　similar　distribution　across　natural　and　adversarial　data　within　the　same　class　and　achieves　higher　overall　robustness　benefiting　from　the　class-discriminative　feature　characteristics.　Experiments　on　various　datasets　demonstrate　that　FAAT　produces　more　discriminative　features　and　performs　favorably　against　state-of-the-art　methods.