The learning with noisy labels has been addressed with both discriminative and generative models. Although discriminative models have dominated the field due to their simpler modeling and more efficient computational training processes, generative models offer a more effective means of disentangling clean and noisy labels and improving the estimation of the label transition matrix. However, generative approaches maximize the joint likelihood of noisy labels and data using a complex formulation that only indirectly optimizes the model of interest associating data and clean labels. Additionally, these approaches rely on generative models that are challenging to train and tend to use uninformative clean label priors. In this paper, we propose a new generative noisy-label learning approach that addresses these three issues. First, we propose a new model optimisation that directly associates data and clean labels. Second, the generative model is implicitly estimated using a discriminative model, eliminating the inefficient training of a generative model. Third, we propose a new informative label prior inspired by partial label learning as supervision signal for noisy label learning. Extensive experiments on several noisy-label benchmarks demonstrate that our generative model provides state-of-the-art results while maintaining a similar computational complexity as discriminative models.

翻译：噪声标签学习已通过判别式模型和生成式模型得到解决。尽管判别式模型因其更简单的建模和更高效的计算训练过程而主导该领域，但生成式模型在分离干净标签与噪声标签以及改进标签转移矩阵估计方面提供了更有效的手段。然而，生成式方法通过复杂公式最大化噪声标签与数据的联合似然，仅间接优化了关联数据与干净标签的目标模型。此外，这些方法依赖难以训练的生成式模型，并倾向于使用无信息的干净标签先验。本文提出一种新型生成式噪声标签学习方法，以解决上述三个问题。首先，我们提出直接关联数据与干净标签的新模型优化方法。其次，通过判别式模型隐式估计生成式模型，消除了生成式模型低效训练的问题。第三，我们提出受部分标签学习启发的信息性标签先验，作为噪声标签学习的监督信号。在多个噪声标签基准上的广泛实验表明，我们的生成式模型在保持与判别式模型相当的计算复杂度的同时，实现了最先进的性能。