Partial label learning (PLL) is a weakly-supervised learning paradigm where
each training instance is paired with a set of candidate labels (partial
label), one of which is the true label. Noisy PLL (NPLL) relaxes this
constraint by allowing some partial labels to not contain the true label,
enhancing the practicality of the problem. Our work centers on NPLL and
presents a minimalistic framework called SARI that initially assigns
pseudo-labels to images by exploiting the noisy partial labels through a
weighted nearest neighbour algorithm. These pseudo-label and image pairs are
then used to train a deep neural network classifier with label smoothing and
standard regularization techniques. The classifier's features and predictions
are subsequently employed to refine and enhance the accuracy of pseudo-labels.
SARI combines the strengths of Average Based Strategies (in pseudo labelling)
and Identification Based Strategies (in classifier training) from the
literature. We perform thorough experiments on seven datasets and compare SARI
against nine NPLL and PLL methods from the prior art. SARI achieves
state-of-the-art results in almost all studied settings, obtaining substantial
gains in fine-grained classification and extreme noise settings.

SARI is a minimalistic framework for noisy partial label learning that combines average-based and identification-based strategies, achieving state-of-the-art results in fine-grained classification and extreme noise settings.

简化的平均与稳健的基于嘈杂部分标签的识别

SARI: Simplistic Average and Robust Identification based Noisy Partial  Label Learning

Partial label learning (PLL) is a typical weakly supervised learning, where
each sample is associated with a set of candidate labels. The basic assumption
of PLL is that the ground-truth label must reside in the candidate set.
However, this assumption may not be satisfied due to the unprofessional
judgment of the annotators, thus limiting the practical application of PLL. In
this paper, we relax this assumption and focus on a more general problem, noisy
PLL, where the ground-truth label may not exist in the candidate set. To
address this challenging problem, we propose a novel framework called
"Iterative Refinement Network (IRNet)". It aims to purify the noisy samples by
two key modules, i.e., noisy sample detection and label correction. Ideally, we
can convert noisy PLL into traditional PLL if all noisy samples are corrected.
To guarantee the performance of these modules, we start with warm-up training
and exploit data augmentation to reduce prediction errors. Through theoretical
analysis, we prove that IRNet is able to reduce the noise level of the dataset
and eventually approximate the Bayes optimal classifier. Experimental results
on multiple benchmark datasets demonstrate the effectiveness of our method.
IRNet is superior to existing state-of-the-art approaches on noisy PLL.

本文提出了一种名为 “Iterative Refinement Network” 的新型框架，旨在加强部分标签学习 (PLL) 中嘈杂样本的纠正，通过嘈杂样本检测和标签修正两个关键模块，确保框架的性能，将噪声 PLL 转换为传统 PLL，实验结果表明 IRNet 在噪声 PLL 上优于现有的最先进方法。