The novel SMate approach leverages GAN minority-class image generators, which benefit from Transfer Learning from majority-class image generators. Consequently, SMate outperforms SMOTE for imbalanced image data-sets. Research at Stanford University, by: Pablo Rodriguez Bertorello, Liang Ping Koh