even here when you have a hundred percent of the labels so the fewer labels the fewer the labels the more benefit you have from the architecture right here and here they show that it's not always optimal to train from the last projection layer but here the first one so they I guess they converge on three projection layers and you always want to keep the first one around after self supervised training as we mentioned before okay they investigate different different distillation losses and show that it is actually important that you