Provably End-to-end Label-noise Learning without Anchor Points

Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:6403-6413, 2021.

Abstract

In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers. Existing consistent estimators for the transition matrix have been developed by exploiting anchor points. However, the anchor-point assumption is not always satisfied in real scenarios. In this paper, we propose an end-to-end framework for solving label-noise learning without anchor points, in which we simultaneously optimize two objectives: the cross entropy loss between the noisy label and the predicted probability by the neural network, and the volume of the simplex formed by the columns of the transition matrix. Our proposed framework can identify the transition matrix if the clean class-posterior probabilities are sufficiently scattered. This is by far the mildest assumption under which the transition matrix is provably identifiable and the learned classifier is statistically consistent. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of the proposed method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-li21l, title = {Provably End-to-end Label-noise Learning without Anchor Points}, author = {Li, Xuefeng and Liu, Tongliang and Han, Bo and Niu, Gang and Sugiyama, Masashi}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {6403--6413}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/li21l/li21l.pdf}, url = {https://proceedings.mlr.press/v139/li21l.html}, abstract = {In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers. Existing consistent estimators for the transition matrix have been developed by exploiting anchor points. However, the anchor-point assumption is not always satisfied in real scenarios. In this paper, we propose an end-to-end framework for solving label-noise learning without anchor points, in which we simultaneously optimize two objectives: the cross entropy loss between the noisy label and the predicted probability by the neural network, and the volume of the simplex formed by the columns of the transition matrix. Our proposed framework can identify the transition matrix if the clean class-posterior probabilities are sufficiently scattered. This is by far the mildest assumption under which the transition matrix is provably identifiable and the learned classifier is statistically consistent. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of the proposed method.} }
Endnote
%0 Conference Paper %T Provably End-to-end Label-noise Learning without Anchor Points %A Xuefeng Li %A Tongliang Liu %A Bo Han %A Gang Niu %A Masashi Sugiyama %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-li21l %I PMLR %P 6403--6413 %U https://proceedings.mlr.press/v139/li21l.html %V 139 %X In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers. Existing consistent estimators for the transition matrix have been developed by exploiting anchor points. However, the anchor-point assumption is not always satisfied in real scenarios. In this paper, we propose an end-to-end framework for solving label-noise learning without anchor points, in which we simultaneously optimize two objectives: the cross entropy loss between the noisy label and the predicted probability by the neural network, and the volume of the simplex formed by the columns of the transition matrix. Our proposed framework can identify the transition matrix if the clean class-posterior probabilities are sufficiently scattered. This is by far the mildest assumption under which the transition matrix is provably identifiable and the learned classifier is statistically consistent. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of the proposed method.
APA
Li, X., Liu, T., Han, B., Niu, G. & Sugiyama, M.. (2021). Provably End-to-end Label-noise Learning without Anchor Points. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6403-6413 Available from https://proceedings.mlr.press/v139/li21l.html.

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