Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities. Without access to true labels, positive points are predicted using pseudo-labels that are inherently noisy, and negative points often require large batches or momentum encoders, resulting in unreliable similarity information and a high computational overhead. In this paper, we rethink a state-of-the-art contrastive PLL method PiCO[24], inspiring the design of a simple framework termed PaPi (Partial-label learning with a guided Prototypical classifier), which demonstrates significant scope for improvement in representation learning, thus contributing to label disambiguation. PaPi guides the optimization of a prototypical classifier by a linear classifier with which they share the same feature encoder, thus explicitly encouraging the representation to reflect visual similarity between categories. It is also technically appealing, as PaPi requires only a few components in PiCO with the opposite direction of guidance, and directly eliminates the contrastive learning module that would introduce noise and consume computational resources. We empirically demonstrate that PaPi significantly outperforms other PLL methods on various image classification tasks.

翻译：在部分标签学习（PLL）中，每个训练实例仅能获得一组包含未知真实标签的模糊候选标签。对比学习通过对比同类/异类实体学习到的表示，近期提升了PLL在视觉任务上的性能。然而，由于无法获取真实标签，正样本需依赖本身含有噪声的伪标签进行预测，而负样本通常需要大批量或动量编码器，这导致相似性信息不可靠且计算开销较高。本文重新审视了当前最先进的对比PLL方法PiCO[24]，受其启发设计了名为PaPi（基于引导原型分类器的部分标签学习）的简单框架。该框架在表示学习方面展现出显著的改进潜力，从而有助于标签消歧。PaPi通过一个线性分类器引导原型分类器的优化，两者共享同一特征编码器，从而显式促使表示反映类别间的视觉相似性。该方法在技术上也颇具吸引力：PaPi仅需在PiCO中引入少量与引导方向相反的组件，并直接消除了会引入噪声且消耗计算资源的对比学习模块。实验证明，PaPi在各种图像分类任务上显著优于其他PLL方法。