There's a good reason that the pictures look similar. Both architectures produce somewhat blurry images.
The problem, in BEGAN's case, is that when your idea of similarity is based of mean squared error, high frequency details are just not important. [1] You can see this by doing PCA on natural image patches. BEGAN uses an autoencoder trained on MSE.
RBMs produce blurry images because the architecture is not good at representing multiplicative interactions. You just get splodges of colour.
The problem, in BEGAN's case, is that when your idea of similarity is based of mean squared error, high frequency details are just not important. [1] You can see this by doing PCA on natural image patches. BEGAN uses an autoencoder trained on MSE.
RBMs produce blurry images because the architecture is not good at representing multiplicative interactions. You just get splodges of colour.
[1] http://danielwaterworth.com/posts/what's-wrong-with-autoenco...