  For classification tasks, deep neural networks are prone to overfitting in
the presence of label noise. Although existing methods are able to alleviate
this problem at low noise levels, they encounter significant performance
reduction at high noise levels, or even at medium noise levels when the label
noise is asymmetric. To train classifiers that are universally robust to all
noise levels, and that are not sensitive to any variation in the noise model,
we propose a distillation-based framework that incorporates a new subcategory
of Positive-Unlabeled learning. In particular, we shall assume that a small
subset of any given noisy dataset is known to have correct labels, which we
treat as "positive", while the remaining noisy subset is treated as
"unlabeled". Our framework consists of the following two components: (1) We
shall generate, via iterative updates, an augmented clean subset with
additional reliable "positive" samples filtered from "unlabeled" samples; (2)
We shall train a teacher model on this larger augmented clean set. With the
guidance of the teacher model, we then train a student model on the whole
dataset. Experiments were conducted on the CIFAR-10 dataset with synthetic
label noise at multiple noise levels for both symmetric and asymmetric noise.
The results show that our framework generally outperforms at medium to high
noise levels. We also evaluated our framework on Clothing1M, a real-world noisy
dataset, and we achieved 2.94% improvement in accuracy over existing
state-of-the-art methods.
