论文标题
P-DIFF：基于概率差异分布的嘈杂标签的学习分类器
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions
论文作者
论文摘要
学习具有嘈杂标签的深神经网络（DNN）分类器是一项具有挑战性的任务，因为由于其高功能，DNN可以轻松地在这些嘈杂的标签上过度贴合。在本文中，我们提出了一个非常简单但有效的培训范式，称为P-DIFF，可以培训DNN分类器，但显然可以减轻嘈杂标签的不利影响。我们提出的概率差异分布隐含地反映了训练样本要清洁的概率，然后采用此概率来重新进行训练过程中的相应样本。即使没有关于训练样本的噪声率的知识，P-DIFF也可以实现良好的性能。基准数据集上的实验还表明，P-DIFF优于最新的样本选择方法。
Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF, which can train DNN classifiers but obviously alleviate the adverse impact of noisy labels. Our proposed probability difference distribution implicitly reflects the probability of a training sample to be clean, then this probability is employed to re-weight the corresponding sample during the training process. P-DIFF can also achieve good performance even without prior knowledge on the noise rate of training samples. Experiments on benchmark datasets also demonstrate that P-DIFF is superior to the state-of-the-art sample selection methods.
