[deepnet]

I found CNN a bit confusing, even though I did guess it meant Convolutional Neural Net.

Perhaps my pre-caffeine morning brain is overly pedantic but Generative Nets use deconvolutions to generate images from latent codes, so using CNN rather than GAN (Generative Adversarial Network) is a bit confusing in this context.

CNNs are used by VAEs (Variational AutoEncoders, also generative) use convolutions to produce the latent codes and the discriminator (adversarial) part of GAN training uses convolutions.

I think Generative Networks ( or GNNs ;-) ) would perhaps have been clearer.

[p1esk]

GANs use transposed convolutions to generate images. I haven’t seen anyone use a true deconvolution operation in deep learning. Not even sure it’s possible to invert the result of a convolutional layer, because it’s lossy.

[lp251]

"Transposed convolution" was called "deconvolution" for a short period of time. The authors apparently weren't aware that "deconvolution" has meant "the inverse problem of recovering a convolved signal" since at least the 70s.