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Part-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1822

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.

Author Information

Xiaobo Xia (The University of Sydney / Xidian University)
Tongliang Liu (The University of Sydney)
Nannan Wang (Xidian University)
Mingming Gong (University of Melbourne)
Haifeng Liu (Brain-Inspired Technology Co., Ltd.)
Gang Niu (RIKEN)
Dacheng Tao (University of Sydney)
Masashi Sugiyama (RIKEN / University of Tokyo)

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