Different from most of existing global HTC methods that rely on pre-specified features (Gopal and Yang, 2013) as input or build on specific models (Cai and Hofmann, 2004;Vens et al., 2008;Silla Jr and Freitas, 2009), our framework is trained in an end-to-end manner by leveraging a differentiable feature representation learning model as the base model.
at the time of inference, we greedily select labels with the highest probability asL x i .