  Label noise is one of the key factors that lead to the poor generalization of
deep learning models. Existing label-noise learning methods usually assume that
the ground-truth classes of the training data are balanced. However, the
real-world data is often imbalanced, leading to the inconsistency between
observed and intrinsic class distribution with label noises. In this case, it
is hard to distinguish clean samples from noisy samples on the intrinsic tail
classes with the unknown intrinsic class distribution. In this paper, we
propose a learning framework for label-noise learning with intrinsically
long-tailed data. Specifically, we propose two-stage bi-dimensional sample
selection (TABASCO) to better separate clean samples from noisy samples,
especially for the tail classes. TABASCO consists of two new separation metrics
that complement each other to compensate for the limitation of using a single
metric in sample separation. Extensive experiments on benchmarks demonstrate
the effectiveness of our method. Our code is available at
https://github.com/Wakings/TABASCO.
