helps a lot with convergence speed and we sort of students across different architectures and across our different tasks and so what's really interesting here you didn't very and grains from the imagenet transfer the pre train weights exactly and and it was per later so not across the entire architecture that wouldn't make sense but sort of perlier take this and then initialize and so what's really interesting is that yeah this is this is a feature independent property because we're sampling i idea we've kind of destroyed all of the