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Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu

Thu Jul 21 03:00 PM -- 05:00 PM (PDT) @ Hall E #530

In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited to learn a clean label classifier by employing the noisy data. Motivated by that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i.e., Bayes-label transition matrix (BLTM)) and learn a classifier to predict Bayes optimal labels. Note that given only noisy data, it is ill-posed to estimate either the CLTM or the BLTM. But favorably, Bayes optimal labels have less uncertainty compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the BLTM, i.e., (a) a set of examples with theoretically guaranteed Bayes optimal labels can be collected out of noisy data; (b) the feasible solution space is much smaller. By exploiting the advantages, we estimate the BLTM parametrically by employing a deep neural network, leading to better generalization and superior classification performance.

Author Information

Shuo Yang (University of Technology Sydney)
Erkun Yang (Xidian University)
Yang Liu (UC Santa Cruz)
Min Xu (Univeristy of Technology Sydney)
Gang Niu (RIKEN)
Gang Niu

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

Tongliang Liu (The University of Sydney)

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