In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to. The goal of this paper is to derive a novel framework of complementary-label learning with an unbiased estimator of the classification risk, for arbitrary losses and models---all existing methods have failed to achieve this goal. Not only is this beneficial for the learning stage, it also makes model/hyper-parameter selection (through cross-validation) possible without the need of any ordinarily labeled validation data, while using any linear/non-linear models or convex/non-convex loss functions. We further improve the risk estimator by a non-negative correction and gradient ascent trick, and demonstrate its superiority through experiments.
Author Information
Takashi Ishida (The University of Tokyo / RIKEN)
Gang Niu (RIKEN)
Aditya Menon (Australian National University)
Masashi Sugiyama (RIKEN / The University of Tokyo)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Complementary-Label Learning for Arbitrary Losses and Models »
Thu Jun 13th 09:25 -- 09:30 AM Room Room 103
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