Progressive generative adversarial networks with reliable sample identification

•We propose a principled model to alleviate the unstable problem of training GANs.•We consider the quality of each samples by identifying reliable sample according to its loss.•We conduct extensive experiments over challenging datasets to validate the effectiveness of our proposed models.•The model is so flexible that could be extended to varying frameworks of GANs.