Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-truth label is always among the candidate label set would be unrealistic, as the reliability of the candidate label sets in real-world applications cannot be guaranteed by annotators. Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set. Due to the challenges posed by unreliable labeling, previous PLL methods will experience a marked decline in performance when applied to UPLL. To address the issue, we propose a two-stage framework named Unreliable Partial Label Learning with Recursive Separation (UPLLRS). In the first stage, the self-adaptive recursive separation strategy is proposed to separate the training set into a reliable subset and an unreliable subset. In the second stage, a disambiguation strategy is employed to progressively identify the ground-truth labels in the reliable subset. Simultaneously, semi-supervised learning methods are adopted to extract valuable information from the unreliable subset. Our method demonstrates state-of-the-art performance as evidenced by experimental results, particularly in situations of high unreliability.
翻译:部分标签学习(PLL)是一个典型的薄弱的学习问题,每个情况都与候选人标签挂钩,其中只有一个是真实的。然而,假定地面真相标签总是在候选人标签中,这是不现实的,因为真实世界应用程序中候选人标签的可靠性得不到通知员的保证。因此,提议了一个名为不可信赖的部分标签学习(UPLL)的通用PLL(UPLL),其中真正的标签可能不在候选人标签中。由于不可靠的标签所构成的挑战,以前的PLL方法在应用到UPLL时,其性能将明显下降。为了解决这个问题,我们提议了一个名为不可再精确分离的部分标签学习(UPLLRS)的两阶段框架。在第一阶段,建议自我调整的循环分离战略将培训分为一个可靠的子集和不可靠的子集。在第二阶段,采用不清晰的战略,逐步确定可靠子集中的地面标签。同时,半封闭的半封闭式标签学习框架名为“不可靠”的实验性能分析结果,通过我们所证实的高级的精确性能展示的方法。