About this question
Starting from my own understanding, and scoped to the purpose of image generation, I'm well aware of the major architectural differences:
A GAN's generator samples from a relatively low dimensional random variable and produces an image. Then the discriminator takes that image and predicts whether the image belongs to a target distribution or not. Once trained, I can generate a variety of images just by sampling the initial random variable and forwarding through the generator.
A VAE's encoder takes an image from a target distribution and compresses it into a low dimensional latent space. Then the decoder's job is to take that latent space representation and reproduce the original image. Once the network is trained, I can generate latent space representations of various images, and interpolate between these before forwarding through the decoder which produces new images.
What I'm more interested in is the consequences of said architectural differences. Why would I choose one approach over the other? And why? (for example, if GANs typically produce better quality images, any ideas why that is so? is it true in all cases or just some?)
