  The success of deep learning depends on large-scale and well-curated training
data, while data in real-world applications are commonly long-tailed and noisy.
Many methods have been proposed to deal with long-tailed data or noisy data,
while a few methods are developed to tackle long-tailed noisy data. To solve
this, we propose a robust method for learning from long-tailed noisy data with
sample selection and balanced loss. Specifically, we separate the noisy
training data into clean labeled set and unlabeled set with sample selection,
and train the deep neural network in a semi-supervised manner with a balanced
loss based on model bias. Extensive experiments on benchmarks demonstrate that
our method outperforms existing state-of-the-art methods.
