  In this paper, we study the problem of learning image classification models
in the presence of label noise. We revisit a simple compression regularization
named Nested Dropout. We find that Nested Dropout, though originally proposed
to perform fast information retrieval and adaptive data compression, can
properly regularize a neural network to combat label noise. Moreover, owing to
its simplicity, it can be easily combined with Co-teaching to further boost the
performance.
  Our final model remains simple yet effective: it achieves comparable or even
better performance than the state-of-the-art approaches on two real-world
datasets with label noise which are Clothing1M and ANIMAL-10N. On Clothing1M,
our approach obtains 74.9% accuracy which is slightly better than that of
DivideMix. On ANIMAL-10N, we achieve 84.1% accuracy while the best public
result by PLC is 83.4%. We hope that our simple approach can be served as a
strong baseline for learning with label noise. Our implementation is available
at https://github.com/yingyichen-cyy/Nested-Co-teaching.
