  Supervised deep learning depends on massive accurately annotated examples,
which is usually impractical in many real-world scenarios. A typical
alternative is learning from multiple noisy annotators. Numerous earlier works
assume that all labels are noisy, while it is usually the case that a few
trusted samples with clean labels are available. This raises the following
important question: how can we effectively use a small amount of trusted data
to facilitate robust classifier learning from multiple annotators? This paper
proposes a data-efficient approach, called \emph{Trustable Co-label Learning}
(TCL), to learn deep classifiers from multiple noisy annotators when a small
set of trusted data is available. This approach follows the coupled-view
learning manner, which jointly learns the data classifier and the label
aggregator. It effectively uses trusted data as a guide to generate trustable
soft labels (termed co-labels). A co-label learning can then be performed by
alternately reannotating the pseudo labels and refining the classifiers. In
addition, we further improve TCL for a special complete data case, where each
instance is labeled by all annotators and the label aggregator is represented
by multilayer neural networks to enhance model capacity. Extensive experiments
on synthetic and real datasets clearly demonstrate the effectiveness and
robustness of the proposed approach. Source code is available at
https://github.com/ShikunLi/TCL
