  Real-world datasets commonly have noisy labels, which negatively affects the
performance of deep neural networks (DNNs). In order to address this problem,
we propose a label noise robust learning algorithm, in which the base
classifier is trained on soft-labels that are produced according to a
meta-objective. In each iteration, before conventional training, the
meta-objective reshapes the loss function by changing soft-labels, so that
resulting gradient updates would lead to model parameters with minimum loss on
meta-data. Soft-labels are generated from extracted features of data instances,
and the mapping function is learned by a single layer perceptron (SLP) network,
which is called MetaLabelNet. Following, base classifier is trained by using
these generated soft-labels. These iterations are repeated for each batch of
training data. Our algorithm uses a small amount of clean data as meta-data,
which can be obtained effortlessly for many cases. We perform extensive
experiments on benchmark datasets with both synthetic and real-world noises.
Results show that our approach outperforms existing baselines.
