[fwilliams]

Okay so I went and read the paper. They discuss generative modeling in section 5 and in the appendix (section 7.2).

Section 5 claims "the corresponding CoordConv GAN model generates objects that better cover the 2D Cartesian space while using 7% of the parameters of the conv GAN". There isn't really quantitative analysis beyond a couple of small graphs discussing this any further. Section 7.2 and 7.3 visually compares the results between the generator's output of interpolated noise vectors in the latent space. The results look good but without quantitative analysis, they are very preliminary.

Generative modeling is tricky and I think in your first comment, the jump from a few nice images to CoordConv can "significantly improve the quality of the representations" is a big one given the sparsity of evidence in the paper. I'm not saying that you're wrong but your original comment seemed a bit misleading to me.

[cs702]

Yes, the evidence is preliminary and not extensive. Yes, generative models can be tricky (to say the least). No one's claiming otherwise.

Visual evidence is important for generative image tasks, given that we can't measure any of these DNN generators against a "true" statistical model that generates the data.

For a DNN to be able to generate more realistic transformations of generated images from low-dimensional representations, it must learn higher quality representations... or are you saying otherwise?