As　developments　in　the　field　of　computer　vision　continue　to　be　achieved,　there　is　a　need　for　more　flexible　strategies　to　cope　with　the　large-scale　and　dynamic　properties　of　real-world　object　categorization　situations.　However,　regarding　most　existing　traditional　incremental　learning　methods,　they　ignore　the　rich　information　of　the　previous　tasks　embedded　in　the　trained　model　during　the　continuous　learning　process.　By　innovatively　combining　model　inversion　and　generative　adversarial　networks,　this　paper　proposes　a　model　inversion-based　generation　technique,　which　makes　the　information　contained　in　the　images　generated　by　the　generator　more　informative.　To　be　specific,　the　information　in　the　model,　which　has　been　trained　by　the　previous　task,　can　be　inverted　into　an　image,　which　can　be　added　to　the　training　process　of　the　generative　network.　The　experimental　results　show　that　the　proposed　method　alleviates　the　catastrophic　forgetting　problem　in　incremental　learning　and　outperforms　other　traditional　methods.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.