  In this paper, we propose a method for training neural networks when we have
a large set of data with weak labels and a small amount of data with true
labels. In our proposed model, we train two neural networks: a target network,
the learner and a confidence network, the meta-learner. The target network is
optimized to perform a given task and is trained using a large set of unlabeled
data that are weakly annotated. We propose to control the magnitude of the
gradient updates to the target network using the scores provided by the second
confidence network, which is trained on a small amount of supervised data. Thus
we avoid that the weight updates computed from noisy labels harm the quality of
the target network model.
