Intelligent　fault　diagnosis　has　been　a　promising　way　for　condition-based　maintenance.　However,　the　small　sample　problem　has　limited　the　application　of　intelligent　fault　diagnosis　into　real　industrial　manufacturing.　Recently,　the　generative　adversarial　network　(GAN)　is　considered　as　a　promising　way　to　solve　the　problem　of　small　sample.　For　this　purpose,　this　paper　reviews　the　related　research　results　on　small-sample-focused　fault　diagnosis　methods　using　the　GAN.　First,　a　systematic　description　of　the　GAN,　and　its　variants,　including　structure-focused　and　loss-focused　improvements,　are　introduced　in　the　paper.　Second,　the　paper　reviews　the　related　GAN-based　intelligent　fault　diagnosis　methods　and　classifies　these　studies　into　three　main　categories,　deep　generative　adversarial　networks　for　data　augmentation,　adversarial　training　for　transfer　learning,　and　other　application　scenarios　(including　GAN　for　anomaly　detection　and　semi-supervised　adversarial　learning).　Finally,　the　paper　discusses　several　limitations　of　existing　studies　and　points　out　future　perspectives　of　GAN-based　applications.　©　2021　ISA