From what I understand there are two networks in a GAN like this one. One (the discriminator) is trained with a bunch of images showing what beetles can look l

From what I understand there are two networks in a GAN like this one. One (the discriminator) is trained with a bunch of images showing what beetles can look like. It detects a real or fake image of a beetle. The other (the generator) is just generating images with a convolutional neural network. The generator optimizes itself based on how close it is to passing the discriminators test - that is its "loss function". So over time, the generator gets better and better at making things that look like beetles. The process takes a very long time and is aided by many GPUs (as mentioned in the article)