Domain　generalization　(DG)　is　a　fundamental　yet　very　challenging　research　topic　in　machine　learning.　The　existing　arts　mainly　focus　on　learning　domain-invariant　features　with　limited　source　domains　in　a　static　model.　Unfortunately,　there　is　a　lack　of　training-free　mechanism　to　adjust　the　model　when　generalized　to　the　agnostic　target　domains.　To　tackle　this　problem,　we　develop　a　brand-new　DG　variant,　namely　Dynamic　Domain　Generalization　(DDG),　in　which　the　model　learns　to　twist　the　network　parameters　to　adapt　to　the　data　from　different　domains.　Specifically,　we　leverage　a　meta-adjuster　to　twist　the　network　parameters　based　on　the　static　model　with　respect　to　different　data　from　different　domains.　In　this　way,　the　static　model　is　optimized　to　learn　domain-shared　features,　while　the　meta-adjuster　is　designed　to　learn　domain-specific　features.　To　enable　this　process,　DomainMix　is　exploited　to　simulate　data　from　diverse　domains　during　teaching　the　meta-adjuster　to　adapt　to　the　agnostic　target　domains.　This　learning　mechanism　urges　the　model　to　generalize　to　different　agnostic　target　domains　via　adjusting　the　model　without　training.　Extensive　experiments　demonstrate　the　effectiveness　of　our　proposed　method.　Code　is　available:　https://github.com/MetaVisionLab/DDG.　©　2022　International　Joint　Conferences　on　Artificial　Intelligence.　All　rights　reserved.