Acquiring pixel-level annotations is often limited in applications such as
histology studies that require domain expertise. Various semi-supervised
learning approaches have been developed to work with limited ground truth
annotations, such as the popular teacher-student models. However, hierarchical
prediction uncertainty within the student model (intra-uncertainty) and image
prediction uncertainty (inter-uncertainty) have not been fully utilized by
existing methods. To address these issues, we first propose a novel inter- and
intra-uncertainty regularization method to measure and constrain both inter-
and intra-inconsistencies in the teacher-student architecture. We also propose
a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet)
as the segmentation model. The two-stage structure complements with the
uncertainty regularization strategy to avoid introducing extra modules in
solving uncertainties and the aggregation mechanisms enable multi-scale and
multi-stage feature integration. Comprehensive experimental results over the
MoNuSeg and CRAG datasets show that our PG-FANet outperforms other
state-of-the-art methods and our semi-supervised learning framework yields
competitive performance with a limited amount of labeled data.

提出了一种新的基于不确定性正则化的半监督学习方法，利用像素级标注的有限性和图像预测不确定性来改进分割模型，通过多尺度和多阶段特征融合实现了更好的性能。

基于内部和外部不确定性的特征聚合模型，用于半监督组织病理图像分割

Inter- and intra-uncertainty based feature aggregation model for  semi-supervised histopathology image segmentation

Lifelong learning capabilities are crucial for sentiment classifiers to
process continuous streams of opinioned information on the Web. However,
performing lifelong learning is non-trivial for deep neural networks as
continually training of incrementally available information inevitably results
in catastrophic forgetting or interference. In this paper, we propose a novel
iterative network pruning with uncertainty regularization method for lifelong
sentiment classification (IPRLS), which leverages the principles of network
pruning and weight regularization. By performing network pruning with
uncertainty regularization in an iterative manner, IPRLS can adapta single BERT
model to work with continuously arriving data from multiple domains while
avoiding catastrophic forgetting and interference. Specifically, we leverage an
iterative pruning method to remove redundant parameters in large deep networks
so that the freed-up space can then be employed to learn new tasks, tackling
the catastrophic forgetting problem. Instead of keeping the old-tasks fixed
when learning new tasks, we also use an uncertainty regularization based on the
Bayesian online learning framework to constrain the update of old tasks weights
in BERT, which enables positive backward transfer, i.e. learning new tasks
improves performance on past tasks while protecting old knowledge from being
lost. In addition, we propose a task-specific low-dimensional residual function
in parallel to each layer of BERT, which makes IPRLS less prone to losing the
knowledge saved in the base BERT network when learning a new task. Extensive
experiments on 16 popular review corpora demonstrate that the proposed IPRLS
method sig-nificantly outperforms the strong baselines for lifelong sentiment
classification. For reproducibility, we submit the code and data
at:this https URL

该研究提出了一种基于网络剪枝和权重正则化的不确定性正则化迭代网络剪枝方法，能够使 BERT 模型在处理多个领域不断到达的数据时避免灾难性遗忘和干扰，从而在终生情感分类方面具有显著优势。

基于不确定性正则化的迭代网络剪枝方法用于终身情感分类

Iterative Network Pruning with Uncertainty Regularization for Lifelong  Sentiment Classification

Obtaining object response maps is one important step to achieve
weakly-supervised semantic segmentation using image-level labels. However,
existing methods rely on the classification task, which could result in a
response map only attending on discriminative object regions as the network
does not need to see the entire object for optimizing the classification loss.
To tackle this issue, we propose a principled and end-to-end train-able
framework to allow the network to pay attention to other parts of the object,
while producing a more complete and uniform response map. Specifically, we
introduce the mixup data augmentation scheme into the classification network
and design two uncertainty regularization terms to better interact with the
mixup strategy. In experiments, we conduct extensive analysis to demonstrate
the proposed method and show favorable performance against state-of-the-art
approaches.

本研究提出了一种基于 mixup 数据增强和不确定性正则化的训练框架，实现了使用图像级标记进行弱监督语义分割的目标响应映射获取，并在实验中得到了比现有方法更好的表现。