Multi-view　data　describes　an　image　sample　with　different　modalities　of　features,　thus　provides　a　more　comprehensive　description　of　data.　Its　three　basic　characteristics,　i.e.,　consensus,　complementary　and　redundancy,　determine　its　performances　in　computer　vision　tasks.　In　this　paper,　we　effectively　exploit　the　above　three　characteristics　to　propose　a　deep　learning　scheme　with　joint　shared-and-specific　information　(JSSI)　for　multi-view　clustering.　Aiming　at　facilitating　the　consensus,　JSSI　extracts　shared　information　of　multi-view　data　via　an　adversarial　similarity　constraint,　which　is　realized　by　classification　and　discrimination　interactions.　Aiming　at　reducing　the　redundancy,　JSSI　separate　out　view-specific　features　and　prevent　them　from　interfering　with　the　shared　features　via　a　difference　constraint.　Aiming　at　ensuring　the　complementary,　JSSI　aligns　the　shared　features　and　then　concatenates　them　with　the　specific　features.　We　examine　the　effectiveness　of　JSSI　with　multi-view　clustering　on　real-world　datasets,　such　as　faces　and　indoor　scenes.　Extensive　experiments　and　comparisons　show　that　JSSI　outperforms　other　state-of-the-art　methods　in　most　of　these　datasets.