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Robust Loss Functions for Complementary Labels Learning

In ordinary-label learning, the correct label is given to each training sample. Similarly, a complementary label is also provided for each training sample in complementary-label learning. A complementary label indicates a class that the example does not belong to. Robust learning of classifiers has been investigated from many viewpoints under label noise, but little attention has been paid to complementary-label learning. In this paper, we present a new algorithm of complementary-label learning with the robustness of loss function. We also provide two sufficient conditions on a loss function so that the minimizer of the risk for complementary labels is theoretically guaranteed to be consistent with the minimizer of the risk for ordinary labels. Finally, the empirical results validate our method’s superiority to current state-of-the-art techniques. Especially in cifar10, our algorithm achieves a much higher test accuracy than the gradient ascent algorithm, and the parameters of our model are less than half of the ResNet-34 they used.

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