  Deep neural networks trained with standard cross-entropy loss are more prone
to memorize noisy labels, which degrades their performance. Negative learning
using complementary labels is more robust when noisy labels intervene but with
an extremely slow model convergence speed. In this paper, we first introduce a
bidirectional learning scheme, where positive learning ensures convergence
speed while negative learning robustly copes with label noise. Further, a
dynamic sample reweighting strategy is proposed to globally weaken the effect
of noise-labeled samples by exploiting the excellent discriminatory ability of
negative learning on the sample probability distribution. In addition, we
combine self-distillation to further improve the model performance. The code is
available at \url{https://github.com/chenchenzong/BLDR}.
