the rest at 2x so 4x wider you get more features linear classification now gets all the way up to 76.5% which is as good as a supervisor learner can be at that it's it is using more features it's using more parameters so it's in the same parameter scenario is not as good as supervised but if you just have a confirm for more parameters it's almost as good as supervised and this this kind of result was lacking for a long long time image image classification sub supervised learning was always lagging behind in supervised learning so so