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

The real-world data tends to be heavily imbalanced and severely skew the
data-driven deep neural networks, which makes Long-Tailed Recognition (LTR) a
massive challenging task. Existing LTR methods seldom train Vision Transformers
(ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of
ViTs always leads to unfair comparisons. In this paper, we systematically
investigate the ViTs' performance in LTR and propose LiVT to train ViTs from
scratch only with LT data. With the observation that ViTs suffer more severe
LTR problems, we conduct Masked Generative Pretraining (MGP) to learn
generalized features. With ample and solid evidence, we show that MGP is more
robust than supervised manners. In addition, Binary Cross Entropy (BCE) loss,
which shows conspicuous performance with ViTs, encounters predicaments in LTR.
We further propose the balanced BCE to ameliorate it with strong theoretical
groundings. Specially, we derive the unbiased extension of Sigmoid and
compensate extra logit margins to deploy it. Our Bal-BCE contributes to the
quick convergence of ViTs in just a few epochs. Extensive experiments
demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well
without any additional data and outperforms comparable state-of-the-art methods
significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist
2018 without bells and whistles. Code is available at
this https URL

该论文介绍了一种名为 LiVT 的模型，使用 Long-Tailed 数据进行训练，其中使用了 Masked Generative Pretraining (MGP) 和 Balanced Binary Cross Entropy (Bal-BCE) 来优化模型性能，结果表明该模型在 iNaturalist 2018 上的 Top-1 准确率可达 81.0%。