1. Disentangled representations

The original GAN was able to generate meaningful outputs, but the downside was that its attributes couldn't be controlled. For example, if we trained a GAN to learn a distribution of celebrity faces, the generator would produce new images of celebrity-looking people. However, there is no way to influence the generator regarding the specific attributes of the face that we want. For example, we're unable to ask the generator for a face of a female celebrity with long black hair, a fair complexion, brown eyes, and who is smiling. The fundamental reason for this is because the 100-dim noise code that we use entangles all of the salient attributes of the generator outputs. We can recall that in tf.keras, the 100-dim code was generated by the random sampling of uniform noise distribution:

        # generate fake images from noise using generator 
        # generate noise using uniform distribution
        noise...