Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL) model achieved state-of-the-art performance. Intuitively, better performance could be achieved if stronger SS strategies and SSL models are employed. Following this intuition, one might easily derive various effective noisy-label learning methods using different combinations of SS strategies and SSL models, which is, however, reinventing the wheel in essence. To prevent this problem, we propose SemiNLL, a versatile framework that combines SS strategies and SSL models in an end-to-end manner. Our framework can absorb various SS strategies and SSL backbones, utilizing their power to achieve promising performance. We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.
翻译:以吵闹标签进行深思熟虑是一项艰巨的任务。 最近在特定抽样选择(SS)战略和特定半监督学习(SSL)模式基础上采用的一些突出方法取得了最先进的业绩。 直观地说,如果采用更强的SS战略和SSL模式,就可以取得更好的业绩。 根据这种直觉,人们可以很容易地利用SSS战略和SSL模式的不同组合来获得各种有效的吵闹标签学习方法,而SSS战略和SSL模式实质上是重塑轮子的本质。 为了防止这一问题,我们提议了SEMNLL, 这是一种将SS战略和SSL模型结合到 SSL 模型的多功能框架。 我们的框架可以吸收各种SSS战略和SSL骨干, 利用它们的力量实现有希望的业绩。 我们还用不同的组合来回动我们的框架, 它将新的艺术状态设置在基准模拟和真实世界数据集上, 以及噪音标签。