Infrared　thermography　techniques　with　thermographic　data　analysis　have　been　widely　applied　to　non-destructive　tests　and　evaluations　of　subsurface　defects　in　practical　composite　materials.　However,　the　performance　of　these　methods　is　still　restricted　by　limited　informative　images　and　difficulties　in　feature　extraction　caused　by　inhomogeneous　backgrounds　and　noise.　In　this　work,　a　novel　generative　manifold　learning　thermography　(GMLT)　is　proposed　for　defect　detection　and　the　evaluation　of　composites.　Specifically,　the　spectral　normalized　generative　adversarial　networks　serve　as　an　image　augmentation　strategy　to　learn　the　thermal　image　distribution,　thereby　generating　virtual　images　to　enrich　the　dataset.　Subsequently,　the　manifold　learning　method　is　employed　for　the　unsupervised　dimensionality　reduction　in　all　images.　Finally,　the　partial　least　squares　regression　is　presented　to　extract　the　explicit　mapping　of　manifold　learning　for　defect　visualization.　Moreover,　probability　density　maps　and　quantitative　metrics　are　proposed　to　evaluate　and　explain　the　obtained　defect　detection　performance.　Experimental　results　on　carbon　fiber-reinforced　polymers　demonstrate　the　superiorities　of　GMLT,　compared　with　other　methods.