Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label miscorrection, resulting in unavoidable performance degradation. Moreover, every training step requires at least three back-propagations, significantly slowing down the training speed. To mitigate these issues, we propose a robust and efficient method that learns a label transition matrix on the fly. Employing the transition matrix makes the classifier skeptical about all the corrected samples, which alleviates the miscorrection issue. We also introduce a two-head architecture to efficiently estimate the label transition matrix every iteration within a single back-propagation, so that the estimated matrix closely follows the shifting noise distribution induced by label correction. Extensive experiments demonstrate that our approach shows the best performance in training efficiency while having comparable or better accuracy than existing methods.

本研究提出一种学习时利用标签转移矩阵的方法，通过增加分类器的怀疑性来缓解标签错误纠正问题，并通过引入双头结构实现单个反向传播内每次迭代中高效地估计标签转移矩阵，结果表明该方法具有比现有方法更好的训练效率及相当或更好的准确性。

通过高效过渡矩阵估计学习，以对抗标签错误纠正