  Imperfect labels are ubiquitous in real-world datasets and seriously harm the
model performance. Several recent effective methods for handling noisy labels
have two key steps: 1) dividing samples into cleanly labeled and wrongly
labeled sets by training loss, 2) using semi-supervised methods to generate
pseudo-labels for samples in the wrongly labeled set. However, current methods
always hurt the informative hard samples due to the similar loss distribution
between the hard samples and the noisy ones. In this paper, we proposed PGDF
(Prior Guided Denoising Framework), a novel framework to learn a deep model to
suppress noise by generating the samples' prior knowledge, which is integrated
into both dividing samples step and semi-supervised step. Our framework can
save more informative hard clean samples into the cleanly labeled set. Besides,
our framework also promotes the quality of pseudo-labels during the
semi-supervised step by suppressing the noise in the current pseudo-labels
generating scheme. To further enhance the hard samples, we reweight the samples
in the cleanly labeled set during training. We evaluated our method using
synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the
real-world datasets WebVision and Clothing1M. The results demonstrate
substantial improvements over state-of-the-art methods.
