The progressive growing has been immensely successful in the past and has been used in a myriad of works since then (number of citations :D). We were mostly trying to solve the issue of extensive hyperparameter tuning with the Multi-Scale Gradients. But the paper mentions a totally new problem altogether. This raises two questions for me and I thought of asking them out here:

1.) The problem of phase artifacts. There is slightly less theoretical motivation about progressive growing being the cause of this problem, and it has been attributed to the fact that the network tries to output highest frequency details at all resolutions. It would be very helpful for me if my fellow redditers could provide any more insights into how these two connect to each other.

2.) If this issue existed since the beginning, then what kind of implications does the use of progressive growing has in the state-of-the-art works in other domains such as image2image, vid2vid, colourization networks etc?

For reference -> MSG-GAN paper (https://arxiv.org/abs/1903.06048)