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Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
Songhua Wu · Xiaobo Xia · Tongliang Liu · Bo Han · Mingming Gong · Nannan Wang · Haifeng Liu · Gang Niu

Thu Jul 22 05:30 AM -- 05:35 AM (PDT) @
in Semisupervised Learning 1 »

Learning with noisy labels has attracted a lot of attention in recent years, where the mainstream approaches are in \emph{pointwise} manners. Meanwhile, \emph{pairwise} manners have shown great potential in supervised metric learning and unsupervised contrastive learning. Thus, a natural question is raised: does learning in a pairwise manner \emph{mitigate} label noise? To give an affirmative answer, in this paper, we propose a framework called \emph{Class2Simi}: it transforms data points with noisy \emph{class labels} to data pairs with noisy \emph{similarity labels}, where a similarity label denotes whether a pair shares the class label or not. Through this transformation, the \emph{reduction of the noise rate} is theoretically guaranteed, and hence it is in principle easier to handle noisy similarity labels. Amazingly, DNNs that predict the \emph{clean} class labels can be trained from noisy data pairs if they are first pretrained from noisy data points. Class2Simi is \emph{computationally efficient} because not only this transformation is on-the-fly in mini-batches, but also it just changes loss computation on top of model prediction into a pairwise manner. Its effectiveness is verified by extensive experiments.

Keywords: Semi-supervised learning · Algorithms

Author Information

Songhua Wu (The University of Sydney)
Xiaobo Xia (The University of Sydney)
Tongliang Liu (The University of Sydney)
Bo Han (HKBU / RIKEN)
Mingming Gong (University of Melbourne)
Nannan Wang (Xidian University)
Haifeng Liu (Brain-Inspired Technology Co., Ltd.)
Gang Niu (RIKEN)
Gang Niu

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.

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