While　improving　the　performance　on　the　out-of-distribution　(OOD)　benchmark　dataset,　the　existing　approach　misses　a　portion　of　the　valid　discriminative　information　such　that　it　reduces　the　performance　on　the　same　manifold　OOD　(SMOOD)　data.　The　key　to　addressing　this　problem　is　to　prompt　the　model　to　learn　effective　and　comprehensive　in-distribution　(ID)　semantic　features.　In　this　paper,　two　strategies　are　proposed　to　improve　the　generalization　ability　of　the　model　to　OOD　data.　Firstly,　the　original　samples　are　replaced　by　features　extracted　from　multiple　“semantic　perspectives”　to　obtain　a　comprehensive　semantics　of　the　samples;　Second,　the　mean　and　variance　of　the　batch　samples　are　perturbed　in　the　inference　stage　to　improve　the　sensitivity　of　the　model　to　the　OOD　data.　The　method　we　propose　does　not　employ　OOD　samples,　uses　no　pre-trained　models　in　training,　and　does　not　require　pre-processing　of　samples　during　inference.　Experimental　results　show　that　our　method　enhances　the　semantic　representation　of　ID　data,　surpasses　state-of-the-art　detection　results　on　the　OOD　benchmark　dataset,　and　significantly　improves　the　performance　of　the　model　in　detecting　the　SMOOD　data.　Copyright　©　2022　Jiang　and　Yu.