Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and thus the true label of some collected examples could be unexpectedly outside the assigned candidate label set. In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples. We consider two types of OOC examples in reality, i.e., the closed-set/open-set OOC examples whose true label is inside/outside the known label space. To solve this new PLL problem, we first calculate the wooden cross-entropy loss from candidate and non-candidate labels respectively, and dynamically differentiate the two types of OOC examples based on specially designed criteria. Then, for closed-set OOC examples, we conduct reversed label disambiguation in the non-candidate label set; for open-set OOC examples, we leverage them for training by utilizing an effective regularization strategy that dynamically assigns random candidate labels from the candidate label set. In this way, the two types of OOC examples can be differentiated and further leveraged for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.

本文提出了一种新的部分标签学习方法以学习OOC样例，通过从候选标签和非候选标签分别计算木制交叉熵损失来解决该问题，并根据特定设计的标准动态分化OOC样例类型。基于标签的开放集OOC样例采用动态随机分配候选标签的有效正则化策略进行培训，而基于标签的封闭集OOC样例则采用反向标签消岐。通过广泛的实验，表明我们提出的方法优于现有的PLL方法。

基于混合封闭集和开放集样本的部分标签学习