  The existence of noisy labels in the dataset causes significant performance
degradation for deep neural networks (DNNs). To address this problem, we
propose a Meta Soft Label Generation algorithm called MSLG, which can jointly
generate soft labels using meta-learning techniques and learn DNN parameters in
an end-to-end fashion. Our approach adapts the meta-learning paradigm to
estimate optimal label distribution by checking gradient directions on both
noisy training data and noise-free meta-data. In order to iteratively update
soft labels, meta-gradient descent step is performed on estimated labels, which
would minimize the loss of noise-free meta samples. In each iteration, the base
classifier is trained on estimated meta labels. MSLG is model-agnostic and can
be added on top of any existing model at hand with ease. We performed extensive
experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our
approach outperforms other state-of-the-art methods by a large margin.
