Part-dependent Label Noise: Towards Instance-dependent Label Noise
This addresses the problem of noisy labels in machine learning for applications where annotators make errors based on instance parts, offering an incremental improvement over existing methods.
The paper tackles the challenge of modeling instance-dependent label noise by approximating it with part-dependent label noise, leveraging human cognition of perceiving instances through parts, and demonstrates superior performance over state-of-the-art methods on synthetic and real-world datasets.
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.