The principle of continual relation extraction~(CRE) involves adapting to
emerging novel relations while preserving od knowledge. While current endeavors
in CRE succeed in preserving old knowledge, they tend to fail when exposed to
contaminated data streams. We assume this is attributed to their reliance on an
artificial hypothesis that the data stream has no annotation errors, which
hinders real-world applications for CRE. Considering the ubiquity of noisy
labels in real-world datasets, in this paper, we formalize a more practical
learning scenario, termed as \textit{noisy-CRE}. Building upon this challenging
setting, we develop a noise-resistant contrastive framework named as
\textbf{N}oise-guided \textbf{a}ttack in \textbf{C}ontrative
\textbf{L}earning~(NaCL) to learn incremental corrupted relations. Compared to
direct noise discarding or inaccessible noise relabeling, we present modifying
the feature space to match the given noisy labels via attacking can better
enrich contrastive representations. Extensive empirical validations highlight
that NaCL can achieve consistent performance improvements with increasing noise
rates, outperforming state-of-the-art baselines.

本文提出了一种更实用的关系抽取学习场景，被称为 “noisy-CRE”。针对实际数据集中的噪声标签，提出了一种基于特征空间扰动的噪声抵抗对比学习框架 NaCL，可以学习到增量性的异常关系，同时在噪声率逐渐增加的情况下实现了持续的性能改进，优于现有的基准模型。

用噪声引导攻击增强对比学习：迈向野外连续关系提取

Enhancing Contrastive Learning with Noise-Guided Attack: Towards  Continual Relation Extraction in the Wild

Graph Neural Networks (GNNs) have shown their great ability in modeling graph
structured data. However, real-world graphs usually contain structure noises
and have limited labeled nodes. The performance of GNNs would drop
significantly when trained on such graphs, which hinders the adoption of GNNs
on many applications. Thus, it is important to develop noise-resistant GNNs
with limited labeled nodes. However, the work on this is rather limited.
Therefore, we study a novel problem of developing robust GNNs on noisy graphs
with limited labeled nodes. Our analysis shows that both the noisy edges and
limited labeled nodes could harm the message-passing mechanism of GNNs. To
mitigate these issues, we propose a novel framework which adopts the noisy
edges as supervision to learn a denoised and dense graph, which can down-weight
or eliminate noisy edges and facilitate message passing of GNNs to alleviate
the issue of limited labeled nodes. The generated edges are further used to
regularize the predictions of unlabeled nodes with label smoothness to better
train GNNs. Experimental results on real-world datasets demonstrate the
robustness of the proposed framework on noisy graphs with limited labeled
nodes.

本篇论文研究如何开发一种具有噪声图和有限标记节点的噪声阻抗图神经网络，通过采用噪声边作为监督来学习去噪和密集图的新框架，并利用生成的边来规范化未标记节点的预测，以更好地训练图神经网络。实验结果表明，所提出的框架在具有有限标记节点的噪声图上具有鲁棒性。

面向带有稀疏标签噪声图的鲁棒图神经网络

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

Semi-supervised learning aims to leverage a large amount of unlabeled data
for performance boosting. Existing works primarily focus on image
classification. In this paper, we delve into semi-supervised learning for
object detection, where labeled data are more labor-intensive to collect.
Current methods are easily distracted by noisy regions generated by pseudo
labels. To combat the noisy labeling, we propose noise-resistant
semi-supervised learning by quantifying the region uncertainty. We first
investigate the adverse effects brought by different forms of noise associated
with pseudo labels. Then we propose to quantify the uncertainty of regions by
identifying the noise-resistant properties of regions over different strengths.
By importing the region uncertainty quantification and promoting multipeak
probability distribution output, we introduce uncertainty into training and
further achieve noise-resistant learning. Experiments on both PASCAL VOC and MS
COCO demonstrate the extraordinary performance of our method.

本文提出了一种基于区域不确定性的抗噪半监督学习方法，通过多峰概率分布输出引入不确定性以实现抗噪学习，并在 PASCAL VOC 和 MS COCO 上的实验结果表明了该方法的卓越性能。