The local loss of HiLAP-SL is defined as where T is the lowest label's 449 level of one example and O t estimates the binary cross entropy over the candidate labels C(l t ): Intuitively, HiLAP-SL works as if there were a set of local classifiers, although most of its parameters (except for the label embedding l) are shared by all the labels so that there is no need to train multiple classifiers.
the policy P puts x i at the root label in the beginning.