Curious as to what is the purpose of using transposed convolutions in the DCGAN architecture…
There were a couple of lectures in GANs C1 Week 2 about transposed convolutions in which Prof Zhou explains how they are useful.
In all the cases we’ll see here, the generator typically takes some random noise as input and then turns that into an output (image) that has more information in it than was in the noise. So we need a way to “inflate” the data by creating more of it. There are a number of different approaches to do that. Prof Zhou discusses “upsampling” layers as one such method, but here she introduces us to transposed convolutions which are one of the ways to inflate data that have learnable parameters so that method may work better in many cases.
Here’s a topic on Jason Brownlee’s Machine Learning Mastery website that gives another explanation of the difference between upsampling and transposed convolutions.