面向部分标签学习的有效视觉表示
Towards Effective Visual Representations for Partial-Label Learning
AI总结：
本文提出PaPi框架，通过线性分类器引导原型分类器优化，消除对比学习模块，在部分标签学习的视觉任务上显著提升表示质量与标签消歧性能。
摘要：
在部分标签学习（PLL）中，每个训练实例仅能获取一组包含未知真实标签的模糊候选标签。近年来，对比学习通过对比相同/不同类别的实体来学习表示，从而提升了部分标签学习在视觉任务上的性能。然而，由于无法获取真实标签，正样本通过本质上带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，导致相似性信息不可靠且计算开销较高。在本文中，我们重新审视了先进的对比部分标签学习方法PiCO[24]，并由此设计了一个简单框架PaPi（基于引导原型分类器的部分标签学习），该框架在表示学习方面展现出显著的改进空间，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，两者共享相同的特征编码器，从而明确鼓励表示反映类别之间的视觉相似性。该方法在技术上也很具吸引力，因为PaPi仅需PiCO中的少量组件且引导方向相反，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。我们通过实验证明，PaPi在各种图像分类任务上显著优于其他部分标签学习方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
