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But maybe with 5 million real examples sampled from the distribution CIFAR-10 was sampled from they would in fact see a difference. Maybe the generative model is capturing only a limited slice of the diversity that the ideal model would really see.

It seems like they should have downsampled from an actually large dataset rather than generatively upsampled from a small dataset. Unless I'm missing something?

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I think what you're saying is plausible, but I would expect the different models to diverge at different rates in that case. So, for example, if resnet-real and resnet-ideal stayed within 1% and the other models showed a bigger range, I would be more suspicious that the generative model was simply creating a dataset that was easy for some architectures to learn.

That being said, I think it would have been much better if they compared some non-convolutional architectures just as a sanity check

Edit: after I wrote this, I checked, and ViT-b/4 is actually a transformer architecture, not CNN. So they did this! And it stayed very close to the same error range from ideal as the CNNs. I am much more confident now that what they did is fine

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