Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization power of a classifier by making the classifier be trained to be overfitted to wrong labels. Existing methods on noisy label have focused on modifying classifier training procedure. It results in two possible problems. First, these methods are not applicable to a pre-trained classifier without further access into training. Second, it is not easy to train a classifier and remove all of negative effects from noisy labels simultaneously. From these problems, we suggests a new branch of approach, Noisy Prediction Calibration (NPC) in learning with noisy labels. Through the introduction and estimation of a new type of transition matrix via generative model, NPC corrects the noisy prediction from the pre-trained classifier to the true label as a post-processing scheme. We prove that NPC theoretically aligns with the transition matrix based methods. Yet, NPC provides more accurate pathway to estimate true label, even without involvement in classifier learning. Also, NPC is applicable to any classifier trained with noisy label methods, if training instances and its predictions are available. Our method, NPC, boosts the classification performances of all baseline models on both synthetic and real-world datasets.

该论文提出了一种使用生成模型估计转移矩阵的方法，将预先训练的分类器的嘈杂预测校正为真实标签，从而解决机器学习中嘈杂标签对分类器泛化性能的影响，并且该方法在合成和真实数据集上均提高了所有基线模型的分类性能。

通过生成式模型进行噪声预测校准：从噪声预测到真实标签