论文标题
自我监督的学习学习从noisylabeled数据中学习
Self-semi-supervised Learning to Learn from NoisyLabeled Data
论文作者
论文摘要
当今深度神经网络的显着成功很大程度上取决于大量正确标记的数据。但是，获得高质量的人体标记数据是相当昂贵的，从而导致训练模型的积极研究领域可靠地标签。为了实现这一目标，一方面，许多论文专门用于将嘈杂标签与干净的标签区分开来增加DNN的概括。另一方面，事实证明，在标签不完整时，已经证明了越来越普遍的自我监督学习方法受益于任务。通过“半”，我们将检测到的错误标记的数据视为未标记的数据；通过“自我”，我们选择一种自我监督的技术来进行半监督的学习。在这个项目中，我们设计了更准确地区分清洁和嘈杂的标签的方法，并借用了自我检查的学习的智慧来训练嘈杂的标记数据。
The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data. However, it is rather costly to obtain high-quality human-labeled data, leading to the active research area of training models robust to noisy labels. To achieve this goal, on the one hand, many papers have been dedicated to differentiating noisy labels from clean ones to increase the generalization of DNN. On the other hand, the increasingly prevalent methods of self-semi-supervised learning have been proven to benefit the tasks when labels are incomplete. By 'semi' we regard the wrongly labeled data detected as un-labeled data; by 'self' we choose a self-supervised technique to conduct semi-supervised learning. In this project, we designed methods to more accurately differentiate clean and noisy labels and borrowed the wisdom of self-semi-supervised learning to train noisy labeled data.
