  Label noise and class imbalance commonly coexist in real-world data. Previous
works for robust learning, however, usually address either one type of the data
biases and underperform when facing them both. To mitigate this gap, this work
presents a novel meta-learning based dynamic loss that automatically adjusts
the objective functions with the training process to robustly learn a
classifier from long-tailed noisy data. Concretely, our dynamic loss comprises
a label corrector and a margin generator, which respectively correct noisy
labels and generate additive per-class classification margins by perceiving the
underlying data distribution as well as the learning state of the classifier.
Equipped with a new hierarchical sampling strategy that enriches a small amount
of unbiased metadata with diverse and hard samples, the two components in the
dynamic loss are optimized jointly through meta-learning and cultivate the
classifier to well adapt to clean and balanced test data. Extensive experiments
show our method achieves state-of-the-art accuracy on multiple real-world and
synthetic datasets with various types of data biases, including CIFAR-10/100,
Animal-10N, ImageNet-LT, and Webvision. Code will soon be publicly available.
