In this experiment , we verify that the results in synthetic datasets can translate into data with higher dimensions . While visualizing the learned energy function is not feasible in high - dimensional space , we can verify whether the learned energy function learns relative densities by inspecting the ranking of samples according to their assigned energies . We train on 28 × 28 images of a single handwritten ( a ) Standard GAN ( b ) Energy GAN without regularization ( EGAN - Const ) Figure 2 : Learned energies and samples from baseline models whose discriminator can not retain density information at the optimal . In the sample plots , blue dots indicate generated samples , and red dots indicate real ones .