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To ensure that the data collected from human subjects is entrusted with asecret, rival labels are introduced to conceal the information provided by theparticipants on purpose. The corresponding learning task can be formulated as anoisy partial-label learning problem. However, conventional partial-labellearning (PLL) methods are still vulnerable to the high ratio of noisy partiallabels, especially in a large labelling space. To learn a more robust model, wepresent Adversary-Aware Partial Label Learning and introduce the$\textit{rival}$, a set of noisy labels, to the collection of candidate labelsfor each instance. By introducing the rival label, the predictive distributionof PLL is factorised such that a handy predictive label is achieved with lessuncertainty coming from the transition matrix, assuming the rival generationprocess is known. Nonetheless, the predictive accuracy is still insufficient toproduce an sufficiently accurate positive sample set to leverage the clusteringeffect of the contrastive loss function. Moreover, the inclusion of rivals alsobrings an inconsistency issue for the classifier and risk function due to theintractability of the transition matrix. Consequently, an adversarial teacherwithin momentum (ATM) disambiguation algorithm is proposed to cope with thesituation, allowing us to obtain a provably consistent classifier and riskfunction. In addition, our method has shown high resiliency to the choice ofthe label noise transition matrix. Extensive experiments demonstrate that ourmethod achieves promising results on the CIFAR10, CIFAR100 and CUB200 datasets.

Adversary-Aware Partial label learning with Label distillation