The　limited　information　and　knowledge　inherent　in　small　sample　sizes　often　result　in　poor　generalization　performance　of　constructed　models.　To　address　this　issue,　we　propose　a　novel　method　for　virtual　sample　generation　(VSG)　utilizing　a　regression　enhanced　generative　adversarial　network　(REGAN).　Initially,　the　variational　autoencoder　(VAE)　serves　as　the　generator　within　REGAN,　with　an　additional　regression　layer　incorporated　to　augment　the　regression　information　pertaining　to　virtual　samples.　Subsequently,　both　real　and　virtual　samples　undergo　discrimination　and　prediction　by　the　discriminator,　thereby　reinforcing　the　regression　relationship　among　variables.　Ultimately,　optimal　virtual　samples　are　selected　utilizing　a　co-training　strategy.　The　efficacy　of　our　proposed　method　is　validated　through　experimentation　on　benchmark　datasets.　©　2024　IEEE.