To ensure that the data collected from human subjects is entrusted with a
secret, rival labels are introduced to conceal the information provided by the
participants on purpose. The corresponding learning task can be formulated as a
noisy partial-label learning problem. However, conventional partial-label
learning (PLL) methods are still vulnerable to the high ratio of noisy partial
labels, especially in a large labelling space. To learn a more robust model, we
present Adversary-Aware Partial Label Learning and introduce the
$\textit{rival}$, a set of noisy labels, to the collection of candidate labels
for each instance. By introducing the rival label, the predictive distribution
of PLL is factorised such that a handy predictive label is achieved with less
uncertainty coming from the transition matrix, assuming the rival generation
process is known. Nonetheless, the predictive accuracy is still insufficient to
produce an sufficiently accurate positive sample set to leverage the clustering
effect of the contrastive loss function. Moreover, the inclusion of rivals also
brings an inconsistency issue for the classifier and risk function due to the
intractability of the transition matrix. Consequently, an adversarial teacher
within momentum (ATM) disambiguation algorithm is proposed to cope with the
situation, allowing us to obtain a provably consistent classifier and risk
function. In addition, our method has shown high resiliency to the choice of
the label noise transition matrix. Extensive experiments demonstrate that our
method achieves promising results on the CIFAR10, CIFAR100 and CUB200 datasets.

提出了对手感知部分标签学习方法 (Adversary-Aware Partial Label Learning)，将对手标签引入候选标签集合，通过引入对手标签，分解 PLL 的预测分布，从而获得较低的不确定性，提高预测标签的可靠性。我们还提出了 ATM 算法以实现分类器和风险函数的一致性，并在 CIFAR10、CIFAR100 和 CUB200 数据集上展开了广泛的实验，表明我们的方法具有高度的鲁棒性。