Actually, there is a formal method to evaluate generative models. All you need is a validation set. Then you need to find what the odds are that the model would

Actually, there is a formal method to evaluate generative models. All you need is a validation set. Then you need to find what the odds are that the model would have generated your validation set. That probability is the one you want to maximize [0]. In order to be able to calculate or estimate this probability, you need to make some amendments to your model architecture. But for instance PixelCNN's know the probability exactly, variational auto-encoders have a lower bound on the probability, etc. It is mainly the generative adverserial networks (GANs) which cannot. And this is the reason research in the latter is limited, despite the remarkable visualizations it produces. But the judging of models by their visualizations is something which is still done too often, and it annoys quite some researchers in the area that those papers still pass review at machine learning conferences. They should be sent to computer graphics conferences, because that is what they actually do /vent. [0] https://arxiv.org/abs/1511.01844 https://arxiv.org/abs/1511.01844