The　ever-increasing　size　of　social　images　and　their　corresponding　imperfect　labels　have　made　social　image　refinement　and　annotation　a　crucial　problem　in　supervised　learning.　However,　previous　models　based　on　nearest　neighbors　or　matrix　completion　are　limited　when　the　social　image　set　is　huge　and　labels　are　highly　sparse.　Deep　generative　models　utilize　inference　and　generative　networks　to　infer　latent　variables　by　introducing　an　observed　data　variable;　they　can　handle　imperfect　data,　capture　noisy　data,　and　fill　in　missing　data　variables.　In　this　paper,　we　propose　a　new　social　image　refinement　and　annotation　model　based　on　the　weakly-supervised　variational　auto-encoder　generative　model.　First,　we　formulate　the　social　image　refinement　and　annotation　problem　as　a　joint　distribution　of　social　images　and　labels　in　a　probabilistic　generative　model.　Secondly,　we　derive　a　new　evidence　lower　bound　object　to　handle　imperfect　labels.　Thirdly,　we　design　a　new　multi-layer　neural　network　including　inference　and　generative　networks　to　optimize　the　new　evidence　lower　bound　efficiently.　Finally,　we　perform　a　comparison　of　our　model　with　other　representative　models　on　several　real-world　social　image　datasets.　Experimental　results　on　social　image　refinement　and　annotation　tasks　show　that　the　proposed　model　is　competitive　or　even　better　than　existing　state-of-the-arts.　©　2019　Elsevier　B.V.