Any ML engineers here? Need help with a conceptual question In a CycleGAN architecture the final layers are made up of downsampling conv layers that act as classifier layers. In most other architectures I've seen that dense (fully-connected) layers are attached to the end of CNNs for classification purposes. What's the difference between the two?