  In contrast to the standard learning paradigm where all classes can be
observed in training data, learning with augmented classes (LAC) tackles the
problem where augmented classes unobserved in the training data may emerge in
the test phase. Previous research showed that given unlabeled data, an unbiased
risk estimator (URE) can be derived, which can be minimized for LAC with
theoretical guarantees. However, this URE is only restricted to the specific
type of one-versus-rest loss functions for multi-class classification, making
it not flexible enough when the loss needs to be changed with the dataset in
practice. In this paper, we propose a generalized URE that can be equipped with
arbitrary loss functions while maintaining the theoretical guarantees, given
unlabeled data for LAC. To alleviate the issue of negative empirical risk
commonly encountered by previous studies, we further propose a novel
risk-penalty regularization term. Experiments demonstrate the effectiveness of
our proposed method.
