  Complementary Labels Learning (CLL) arises in many real-world tasks such as
private questions classification and online learning, which aims to alleviate
the annotation cost compared with standard supervised learning. Unfortunately,
most previous CLL algorithms were in a stable environment rather than an open
and dynamic scenarios, where data collected from unseen augmented classes in
the training process might emerge in the testing phase. In this paper, we
propose a novel problem setting called Complementary Labels Learning with
Augmented Classes (CLLAC), which brings the challenge that classifiers trained
by complementary labels should not only be able to classify the instances from
observed classes accurately, but also recognize the instance from the Augmented
Classes in the testing phase. Specifically, by using unlabeled data, we propose
an unbiased estimator of classification risk for CLLAC, which is guaranteed to
be provably consistent. Moreover, we provide generalization error bound for
proposed method which shows that the optimal parametric convergence rate is
achieved for estimation error. Finally, the experimental results on several
benchmark datasets verify the effectiveness of the proposed method.
