We present one of the preliminary NLP works under the challenging setup of
Learning from Label Proportions (LLP), where the data is provided in an
aggregate form called bags and only the proportion of samples in each class as
the ground truth. This setup is inline with the desired characteristics of
training models under Privacy settings and Weakly supervision. By
characterizing some irregularities of the most widely used baseline technique
DLLP, we propose a novel formulation that is also robust. This is accompanied
with a learnability result that provides a generalization bound under LLP.
Combining this formulation with a self-supervised objective, our method
achieves better results as compared to the baselines in almost 87% of the
experimental configurations which include large scale models for both long and
short range texts across multiple metrics.

在隐私设置和弱监督下，我们提出了一种新的基线技术 DLLP 的不规则性，以及与 LLP 下的通用性边界争论相结合的基于自我监督目标的新公式，该方法在 87% 的实验配置中比基线模型取得更好的结果，实验涵盖了长篇和短篇文本的大规模模型，并使用多个度量标准进行评估。

基于标签比例的文本分类学习

Learning under Label Proportions for Text Classification

The annotation of 3D datasets is required for semantic-segmentation and
object detection in scene understanding. In this paper we present a framework
for the weakly supervision of a point clouds transformer that is used for 3D
object detection. The aim is to decrease the required amount of supervision
needed for training, as a result of the high cost of annotating a 3D datasets.
We propose an Unsupervised Voting Proposal Module, which learns randomly preset
anchor points and uses voting network to select prepared anchor points of high
quality. Then it distills information into student and teacher network. In
terms of student network, we apply ResNet network to efficiently extract local
characteristics. However, it also can lose much global information. To provide
the input which incorporates the global and local information as the input of
student networks, we adopt the self-attention mechanism of transformer to
extract global features, and the ResNet layers to extract region proposals. The
teacher network supervises the classification and regression of the student
network using the pre-trained model on ImageNet. On the challenging KITTI
datasets, the experimental results have achieved the highest level of average
precision compared with the most recent weakly supervised 3D object detectors.

本文提出了一种用于 3D 物体检测的弱监督点云变换器框架，旨在降低对训练所需监督的数量，以减少对 3D 数据集进行注释的高成本，通过使用投票网络选择高质量的预设锚点，将信息提炼到学生网络和教师网络。