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Context: In Figure 19 (Appendix D), we present truncation plots for models trained on this dataset.Unlike for ImageNet, where truncation limits of \sigma\approx 0 tend to produce the highest fidelity scores, IS is typically maximized for our JFT-300M models when the truncation value \sigma ranges from 0.5 to 1.We suspect that this is at least partially due to the intra-class variability of JFT-300M labels, as well as the relative complexity of the image distribution, which includes images with multiple objects at a variety of scales.Interestingly, unlike models trained on ImageNet, where training tends to collapse without heavy regularization (Section 4), the models trained on JFT-300M remain stable over many hundreds of thousands of iterations.This suggests that moving beyond ImageNet to larger datasets may partially alleviate GAN stability issues.

Question: What is the definition of intra-class variability?
intra-class variability here means images with multiple objects at a variety of scales.