  Label noise will degenerate the performance of deep learning algorithms
because deep neural networks easily overfit label errors. Let X and Y denote
the instance and clean label, respectively. When Y is a cause of X, according
to which many datasets have been constructed, e.g., SVHN and CIFAR, the
distributions of P(X) and P(Y|X) are entangled. This means that the
unsupervised instances are helpful to learn the classifier and thus reduce the
side effect of label noise. However, it remains elusive on how to exploit the
causal information to handle the label noise problem. In this paper, by
leveraging a structural causal model, we propose a novel generative approach
for instance-dependent label-noise learning. In particular, we show that
properly modeling the instances will contribute to the identifiability of the
label noise transition matrix and thus lead to a better classifier.
Empirically, our method outperforms all state-of-the-art methods on both
synthetic and real-world label-noise datasets.
