  We consider the learning from noisy labels (NL) problem which emerges in many
real-world applications. In addition to the widely-studied synthetic noise in
the NL literature, we also consider the pseudo labels in semi-supervised
learning (Semi-SL) as a special case of NL. For both types of noise, we argue
that the generalization performance of existing methods is highly coupled with
the quality of noisy labels. Therefore, we counter the problem from a novel and
unified perspective: learning from the auxiliary clean labels. Specifically, we
propose the Rotational-Decoupling Consistency Regularization (RDCR) framework
that integrates the consistency-based methods with the self-supervised rotation
task to learn noise-tolerant representations. The experiments show that RDCR
achieves comparable or superior performance than the state-of-the-art methods
under small noise, while outperforms the existing methods significantly when
there is large noise.
