TLDR: One can use fake noise-generated images to pre-train detection models. But "noise" here means something like the output of a GAN when triggered with a ran

TLDR: One can use fake noise-generated images to pre-train detection models. But "noise" here means something like the output of a GAN when triggered with a random input, not "TV has no connection noise". I expect this result to be not that interesting for most AI programmers, because you'll use a pretrained ResNet preprocessing anyway. But it is a very elegant solution for the licensing issues that come with large unsupervised image collections like ImageNet.