Continuous Review and Timely Correction: Enhancing the Resistance to Noisy Labels via Self-Not-True and Class-Wise...

Deep neural networks possess remarkable learning capabilities but are vulnerable to overfitting in the presence of mislabeled data. A well-known memorization effect causes networks to first fit clean samples and later memorize noisy labels. Although early stopping can partially alleviate this issue, it cannot prevent the accumulation of incorrect knowledge or recover information lost due to mislabeled inputs. In this paper, we introduce an innovative mechanism for continuous review and timely correction of learned knowledge. Our approach allows the network to repeatedly revisit and reinforce correct information while promptly addressing any inaccuracies stemming from mislabeled data. We present a novel method called self-not-true-distillation (SNTD). This technique employs self-distillation, where the network from previous training iterations acts as a teacher, guiding the current network to review and solidify its understanding of accurate labels. Crucially, SNTD masks the true class label in the logits during this process, concentrating on the non-true classes to correct any erroneous knowledge that may have been acquired. We also recognize that different data classes follow distinct learning trajectories. A single teacher network might struggle to effectively guide the learning of all classes at once, which necessitates selecting different teacher networks for each specific class. Additionally, the influence of the teacher network's guidance varies throughout the training process. To address these challenges, we propose SNTD+, which integrates a class-wise distillation strategy along with a dynamic weight adjustment mechanism. Together, these enhancements significantly bolster SNTD's robustness in tackling complex scenarios characterized by label noise.