  Label noise widely exists in large-scale datasets and significantly
degenerates the performances of deep learning algorithms. Due to the
non-identifiability of the instance-dependent noise transition matrix, most
existing algorithms address the problem by assuming the noisy label generation
process to be independent of the instance features. Unfortunately, noisy labels
in real-world applications often depend on both the true label and the
features. In this work, we tackle instance-dependent label noise with a novel
deep generative model that avoids explicitly modeling the noise transition
matrix. Our algorithm leverages casual representation learning and
simultaneously identifies the high-level content and style latent factors from
the data. By exploiting the supervision information of noisy labels with
structural causal models, our empirical evaluations on a wide range of
synthetic and real-world instance-dependent label noise datasets demonstrate
that the proposed algorithm significantly outperforms the state-of-the-art
counterparts.
