Partial Multi-label Learning (PML) is a type of weakly supervised learning
where each training instance corresponds to a set of candidate labels, among
which only some are true. In this paper, we introduce \our{}, a novel
probabilistic approach to this problem that extends the binary cross entropy to
the PML setup. In contrast to existing methods, it does not require suboptimal
disambiguation and, as such, can be applied to any deep architecture.
Furthermore, experiments conducted on artificial and real-world datasets
indicate that \our{} outperforms existing approaches, especially for high noise
in a candidate set.

这篇论文介绍了一种新颖的概率方法，名为 Partial Multi-label Learning（部分多标签学习），在这种方法中，每个训练实例对应一组候选标签，其中只有一部分是正确的；与现有方法相比，它不需要次优的消岐，因此可以应用于任何深度架构；在人工和真实数据集上进行的实验证明，Partial Multi-label Learning 在候选集中噪声高的情况下表现出色，优于现有方法。

概率部分多标签学习

ProPML: Probability Partial Multi-label Learning

In partial multi-label learning (PML), each data example is equipped with a
candidate label set, which consists of multiple ground-truth labels and other
false-positive labels. Recently, graph-based methods, which demonstrate a good
ability to estimate accurate confidence scores from candidate labels, have been
prevalent to deal with PML problems. However, we observe that existing
graph-based PML methods typically adopt linear multi-label classifiers and thus
fail to achieve superior performance. In this work, we attempt to remove
several obstacles for extending them to deep models and propose a novel deep
Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we
introduce the instance-level and label-level similarities to recover label
confidences as well as exploit label dependencies. At each training epoch,
labels are propagated on the instance and label graphs to produce relatively
accurate pseudo-labels; then, we train the deep model to fit the numerical
labels. Moreover, we provide a careful analysis of the risk functions to
guarantee the robustness of the proposed model. Extensive experiments on
various synthetic datasets and three real-world PML datasets demonstrate that
PLAIN achieves significantly superior results to state-of-the-art methods.

本文提出利用图形解析和深度模型相结合的方法 ——PLAIN 应对 PML 问题，提高其精确性和鲁棒性。该方法在多个合成数据集和三个实际 PML 数据集上都表现出显著的优越性。

基于图形消歧的深度部分多标签学习

Deep Partial Multi-Label Learning with Graph Disambiguation

Partial Multi-label Learning (PML) aims to induce the multi-label predictor
from datasets with noisy supervision, where each training instance is
associated with several candidate labels but only partially valid. To address
the noisy issue, the existing PML methods basically recover the ground-truth
labels by leveraging the ground-truth confidence of the candidate label, \ie
the likelihood of a candidate label being a ground-truth one. However, they
neglect the information from non-candidate labels, which potentially
contributes to the ground-truth label recovery. In this paper, we propose to
recover the ground-truth labels, \ie estimating the ground-truth confidences,
from the label enrichment, composed of the relevance degrees of candidate
labels and irrelevance degrees of non-candidate labels. Upon this observation,
we further develop a novel two-stage PML method, namely
\emph{\underline{P}artial \underline{M}ulti-\underline{L}abel
\underline{L}earning with \underline{L}abel
\underline{E}nrichment-\underline{R}ecovery} (\baby), where in the first stage,
it estimates the label enrichment with unconstrained label propagation, then
jointly learns the ground-truth confidence and multi-label predictor given the
label enrichment. Experimental results validate that \baby outperforms the
state-of-the-art PML methods.

本论文提出一种新的 Partial Multi-label Learning 算法 (aby)，通过利用 candidate label 的相关度和 non-candidate label 的不相关度，重新定义 ground-truth confidence 来降噪，然后在此基础上使用 unconstrained label propagation 和多标签预测器共同完成对 ground-truth confidence 和多标签的学习。实验结果表明，aby 的表现优于现有的 PML 方法。