# Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

arXiv: https://arxiv.org/abs/2310.19247 · HTML: https://mlsanity.com/paper/2310.19247/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 74 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 30 of 39)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Equalization Loss for Long-Tailed Object Recognition](https://mlsanity.com/paper/2003.05176/) (2020)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [Balanced Contrastive Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2207.09052/) (2022)
- [Evaluating Bayesian Deep Learning Methods for Semantic Segmentation](https://mlsanity.com/paper/1811.12709/) (2018)
- [M2m: Imbalanced Classification via Major-to-minor Translation](https://mlsanity.com/paper/2004.00431/) (2020)
- [Targeted Supervised Contrastive Learning for Long-Tailed Recognition](https://mlsanity.com/paper/2111.13998/) (2021)
- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Improving model calibration with accuracy versus uncertainty optimization](https://mlsanity.com/paper/2012.07923/) (2020)
- [Evidential Deep Learning for Open Set Action Recognition](https://mlsanity.com/paper/2107.10161/) (2021)
- [Distributional Robustness Loss for Long-tail Learning](https://mlsanity.com/paper/2104.03066/) (2021)
- [Striking the Right Balance with Uncertainty](https://mlsanity.com/paper/1901.07590/) (2019)
- [Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning](https://mlsanity.com/paper/2001.01385/) (2020)
- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Synthetic Examples Improve Generalization for Rare Classes](https://mlsanity.com/paper/1904.05916/) (2019)
- [Survey of resampling techniques for improving classification performance in unbalanced datasets](https://mlsanity.com/paper/1608.06048/) (2016)

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey on Evidential Deep Learning and Its Applications](https://mlsanity.com/paper/2409.04720/) (2024)
- [Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2312.11891/) (2023)
- [Revisiting Essential and Nonessential Settings of Evidential Deep Learning](https://mlsanity.com/paper/2410.00393/) (2024)
- [Relational Prompt-based Pre-trained Language Models for Social Event Detection](https://mlsanity.com/paper/2404.08263/) (2024)
- [DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism](https://mlsanity.com/paper/2409.00614/) (2024)
- [RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU](https://mlsanity.com/paper/2409.05275/) (2024)
- [Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification](https://mlsanity.com/paper/2409.19214/) (2024)
- [Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space](https://mlsanity.com/paper/2412.10712/) (2024)

## Abstract (arXiv)

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCLSED, and its variant - UCL-ECSED, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012100, Events2018100, and CrisisLexT7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.