Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evidences showing that we humans perceive instances by decomposing them into parts. Annotators are therefore more likely to annotate instances based on the parts rather than the whole instances, where a wrong mapping from parts to classes may cause the instance-dependent label noise. Motivated by this human cognition, in this paper, we approximate the instance-dependent label noise by exploiting \textit{part-dependent} label noise. Specifically, since instances can be approximately reconstructed by a combination of parts, we approximate the instance-dependent \textit{transition matrix} for an instance by a combination of the transition matrices for the parts of the instance. The transition matrices for parts can be learned by exploiting anchor points (i.e., data points that belong to a specific class almost surely). Empirical evaluations on synthetic and real-world datasets demonstrate our method is superior to the state-of-the-art approaches for learning from the instance-dependent label noise.

翻译：使用 \ textit{ instance- 依赖] 标签 来学习标签噪音是困难的, 因为很难模拟这种真实世界的噪音。 请注意, 有心理和生理证据表明, 我们人类通过将它们分解成零块来看待这些现象。 因此, 警告者更有可能根据部件而不是整个例子来做笔记, 在部分之间进行错误的映射可能会引起依赖实例的标签噪音。 本文中, 我们受这种人类认知的驱使, 我们通过利用 \ textit{ part- apart- refait} 标签的噪音来比较依赖实例的标签噪音。 具体地说, 由于这些例子可以通过部件的组合进行大致重建, 我们通过实例的过渡矩阵组合来比较依赖实例的\ textitilit{ 过渡矩阵 。 部分的过渡矩阵可以通过利用锚点( 即属于某一类的数据点几乎肯定) 来学习。 对合成和真实世界的数据集进行经验性评估, 表明我们的方法优于从基于实例的标签学习的状态方法。