Multi-label learning (MLL) requires comprehensive multi-semantic annotations
that is hard to fully obtain, thus often resulting in missing labels scenarios.
In this paper, we investigate Single Positive Multi-label Learning (SPML),
where each image is associated with merely one positive label. Existing SPML
methods only focus on designing losses using mechanisms such as hard
pseudo-labeling and robust losses, mostly leading to unacceptable false
negatives. To address this issue, we first propose a generalized loss framework
based on expected risk minimization to provide soft pseudo labels, and point
out that the former losses can be seamlessly converted into our framework. In
particular, we design a novel robust loss based on our framework, which enjoys
flexible coordination between false positives and false negatives, and can
additionally deal with the imbalance between positive and negative samples.
Extensive experiments show that our approach can significantly improve SPML
performance and outperform the vast majority of state-of-the-art methods on all
the four benchmarks.

本文研究了单正多标签学习（Single Positive Multi-label Learning - SPML）的问题，提出了基于期望风险最小化的广义损失框架，用于生成软伪标签，并设计了一种能够处理正负样本不平衡同时灵活协调假阳性和假阴性的新型鲁棒损失函数。实验证明，该方法能够显著提高 SPML 性能，在四个基准测试上胜过绝大多数最先进的方法。

使用广义鲁棒损失提升单一正标签多标签分类

Boosting Single Positive Multi-label Classification with Generalized  Robust Loss

The cost of data annotation is a substantial impediment for multi-label image
classification: in every image, every category must be labeled as present or
absent. Single positive multi-label (SPML) learning is a cost-effective
solution, where models are trained on a single positive label per image. Thus,
SPML is a more challenging domain, since it requires dealing with missing
labels. In this work, we propose a method to turn single positive data into
fully-labeled data: Pseudo Multi-Labels. Basically, a teacher network is
trained on single positive labels. Then, we treat the teacher model's
predictions on the training data as ground-truth labels to train a student
network on fully-labeled images. With this simple approach, we show that the
performance achieved by the student model approaches that of a model trained on
the actual fully-labeled images.

该研究介绍了一种称为伪多标签的方法，通过教师模型在单个正标签上训练，然后使用其预测作为标记数据来训练学生模型，以解决多标签图像分类中数据注释成本高的问题。研究表明，在实际的全标签数据上训练得到的模型性能可以近似于该方法训练得到的模型。

单正多标记学习的伪标签

Pseudo Labels for Single Positive Multi-Label Learning

Annotating data for multi-label classification is prohibitively expensive
because every category of interest must be confirmed to be present or absent.
Recent work on single positive multi-label (SPML) learning shows that it is
possible to train effective multi-label classifiers using only one positive
label per image. However, the standard benchmarks for SPML are derived from
traditional multi-label classification datasets by retaining one positive label
for each training example (chosen uniformly at random) and discarding all other
labels. In realistic settings it is not likely that positive labels are chosen
uniformly at random. This work introduces protocols for studying label bias in
SPML and provides new empirical results.

该研究介绍了单正多标签学习中标签偏差的研究方法，并提供了新的经验证据，以便使用仅一个正标签的图像进行有效的多标签分类。