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Class-conditional noise commonly exists in machine learning tasks, where theclass label is corrupted with a probability depending on its ground-truth. Manyresearch efforts have been made to improve the model robustness against theclass-conditional noise. However, they typically focus on the single label caseby assuming that only one label is corrupted. In real applications, an instanceis usually associated with multiple labels, which could be corruptedsimultaneously with their respective conditional probabilities. In this paper,we formalize this problem as a general framework of learning withClass-Conditional Multi-label Noise (CCMN for short). We establish two unbiasedestimators with error bounds for solving the CCMN problems, and further provethat they are consistent with commonly used multi-label loss functions.Finally, a new method for partial multi-label learning is implemented withunbiased estimator under the CCMN framework. Empirical studies on multipledatasets and various evaluation metrics validate the effectiveness of theproposed method.

CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise