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.

PaPi 是一种基于线性分类器和原型分类器的简单框架，用于处理部分标签学习任务，通过显式地鼓励表征反映类别间的视觉相似性，以在表示学习中提高性能并促进标签消歧，并已在各种图像分类任务中比其他 PLL 方法表现更优。

针对部分标签学习的有效视觉表征

Towards Effective Visual Representations for Partial-Label Learning

Partial-label learning (PLL) is a peculiar weakly-supervised learning task
where the training samples are generally associated with a set of candidate
labels instead of single ground truth. While a variety of label disambiguation
methods have been proposed in this domain, they normally assume a
class-balanced scenario that may not hold in many real-world applications.
Empirically, we observe degenerated performance of the prior methods when
facing the combinatorial challenge from the long-tailed distribution and
partial-labeling. In this work, we first identify the major reasons that the
prior work failed. We subsequently propose SoLar, a novel Optimal
Transport-based framework that allows to refine the disambiguated labels
towards matching the marginal class prior distribution. SoLar additionally
incorporates a new and systematic mechanism for estimating the long-tailed
class prior distribution under the PLL setup. Through extensive experiments,
SoLar exhibits substantially superior results on standardized benchmarks
compared to the previous state-of-the-art PLL methods. Code and data are
available at: this https URL .

提出了基于最优传输的 SoLar 框架作为 PLL 问题的解决方案，该框架可用于弱监督情况下部分标签学习中的标签消岐，特别是在面对长尾分布和标签不完整情况下，该方法比先前的最新 PLL 方法表现出更高的性能。

SoLar: Sinkhorn 标签提炼在不平衡偏标签学习中的应用

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning

Partial label learning (PLL) is an important problem that allows each
training example to be labeled with a coarse candidate set, which well suits
many real-world data annotation scenarios with label ambiguity. Despite the
promise, the performance of PLL often lags behind the supervised counterpart.
In this work, we bridge the gap by addressing two key research challenges in
PLL -- representation learning and label disambiguation -- in one coherent
framework. Specifically, our proposed framework PiCO consists of a contrastive
learning module along with a novel class prototype-based label disambiguation
algorithm. PiCO produces closely aligned representations for examples from the
same classes and facilitates label disambiguation. Theoretically, we show that
these two components are mutually beneficial, and can be rigorously justified
from an expectation-maximization (EM) algorithm perspective. Moreover, we study
a challenging yet practical noisy partial label learning setup, where the
ground-truth may not be included in the candidate set. To remedy this problem,
we present an extension PiCO+ that performs distance-based clean sample
selection and learns robust classifiers by a semi-supervised contrastive
learning algorithm. Extensive experiments demonstrate that our proposed methods
significantly outperform the current state-of-the-art approaches in standard
and noisy PLL tasks and even achieve comparable results to fully supervised
learning.

本文提出一个 PiCO 框架，包括对比学习模块和新型基于原型的标签消歧算法用于解决部分标签学习的问题。该框架可以对相同类别的样例进行紧密对齐并促进标签消歧，并在嘈杂的部分标签学习任务中表现出色，甚至可以与完全监督的学习相媲美。