The paper introduces a new method called SCARCE for complementary-label learning, addressing overfitting issues and providing superior performance. It explores the relationship between complementary-label learning and negative-unlabeled learning. Extensive experiments validate the effectiveness of SCARCE on synthetic and real-world datasets.
Existing approaches rely on uniform or biased distribution assumptions, which may not hold in real-world scenarios. The proposed SCARCE method does not require these assumptions, offering a more practical solution. The study also investigates the impact of inaccurate class priors on classification performance.
SCARCE outperforms state-of-the-art methods in most cases, demonstrating its robustness and effectiveness in various settings. The theoretical analysis provides insights into the convergence properties and calibration to 0-1 loss.
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