Lack of some critical comparisons: 1) Does the proposed method also outperform the approach that first generates using a pre-trained BigGAN-256 then upsamples using an officially pre-trained ESRGAN? 2) What about the inference time (or complexity) of NSB-GAN compared to BigGAN?
If the decoders are trained with real samples only (drawn from the imagenet dataset), the upsampled outputs may have visual artifacts due to the mismatched distribution between the train (real) and test (fake gen from sampler) images. For example, the images of the cabinet (Fig3, 4, 5th row) have overly sharpened artifacts that BigGAN does not suffer.
Overall, the suggested work effectively decreases the training time of the BigGAN using a simple idea. However, there are missing comparisons and analyses such as 1) comparison with pre-trained BigGAN -> ESRGAN 2) How the capacity of the SR model affects the FID. And lastly, since the proposed method is pipelining, there are some unexpected artifacts.
2) How the capacity of the SR model affects the FID. And lastly, since the proposed method is pipelining, there are some unexpected artifacts.