We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. This framework casts learning with label noise as a form of domain adaptation, in particular, domain adaptation under posterior drift.

Noisy labels are inevitable in large real-world datasets.

We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. This framework casts learning with label noise as a form of domain adaptation, in particular, domain adaptation under posterior drift.

We begin by introducing notation. In the main paper (Section 3), we show how to approximate instance-dependent transition matrix by exploiting part-dependent transition matrices. Note that it is more realistic that different instances have different flip rates. However, it is hard to identify these parameters without any assumption. In the main paper (Section 4), we present the experimental results on four synthetic noisy datasets, i.e., F-MNIST, SVHN, CIF AR-10, and NEWS .