The noise transition matrix plays a central role in the problem of learning from noisy labels. Among many other reasons, a significant number of existing solutions rely on access to it. Estimating the transition matrix without using ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recent works proposing solutions for learning from instance-dependent noisy labels, we lack a unified understanding of when such a problem remains identifiable, and therefore learnable. This paper seeks to provide answers to a sequence of related questions: What are the primary factors that contribute to the identifiability of a noise transition matrix? Can we explain the observed empirical successes? When a problem is not identifiable, what can we do to make it so? We will relate our theoretical findings to the literature and hope to provide guidelines for developing effective solutions for battling instance-dependent label noise.
翻译:噪声过渡矩阵在从吵闹的标签中学习的问题上起着核心作用。 许多其他原因包括,大量现有解决方案都依赖于使用它。 在不使用地面真相标签的情况下估计过渡矩阵是一项关键和具有挑战性的任务。 当标签噪音过渡取决于每个实例时,识别依赖实例的噪音过渡矩阵的问题变得更具挑战性。 尽管最近的工作提出了从依赖实例的吵闹标签中学习的解决方案,但我们对何时可以识别并因此可以学习这一问题缺乏统一的理解。 本文试图提供一系列相关问题的答案:哪些是促成噪音过渡矩阵可识别性的主要因素?我们能解释观察到的经验吗? 当问题无法识别时,我们能做些什么来做到这一点?我们将将我们的理论发现与文献联系起来,并希望为制定有效解决方案以实例为依据的标签噪音提供指导方针。