Labor-intensive labeling becomes a bottleneck in developing computer vision algorithms based on deep learning. For this reason, dealing with imperfect labels has increasingly gained attention and has become an active field of study. We address learning with noisy labels (LNL) problem, which is formalized as a task of finding a structured manifold in the midst of noisy data. In this framework, we provide a proper objective function and an optimization algorithm based on two expectation-maximization (EM) cycles. The separate networks associated with the two EM cycles collaborate to optimize the objective function, where one model is for distinguishing clean labels from corrupted ones while the other is for refurbishing the corrupted labels. This approach results in a non-collapsing LNL-flywheel model in the end. Experiments show that our algorithm achieves state-of-the-art performance in multiple standard benchmarks with substantial margins under various types of label noise.

翻译：标注工作劳动密集，已成为发展基于深度学习的计算机视觉算法的瓶颈。因此，处理不完美标签的问题日益受到关注并成为一个活跃的研究领域。我们研究含噪标签学习（LNL）问题，将其形式化为在含噪数据中寻找结构化流形的任务。在此框架下，我们提出一个合适的目标函数以及基于两个期望最大化（EM）循环的优化算法。与这两个EM循环相关联的独立网络通过协作优化该目标函数，其中一个模型用于区分干净标签与损坏标签，另一个模型则用于修复损坏标签。该方法最终形成一个非坍缩的LNL飞轮模型。实验表明，我们的算法在多种标准基准测试中，针对不同类型的标签噪声，均以显著优势取得了最先进的性能。