动态调整模型结构，精准识别数据中隐藏的异常子群体。
DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection
- 通过自适应聚类机制，动态发现数据中的潜在子群体。
- 在缺失类别场景下仍保持高检测准确率，远超传统方法。
- 适合处理真实世界中不断变化的数据分布，提升模型鲁棒性。
现实观测数据常包含现有或新出现的异质子群体，偏离全局模式。多数模型忽视这些低频群体，导致预测不准甚至有害。现有方法多将此类样本视为域外（OOD）进行检测，而非适应新趋势。本文提出DynaSubVAE，一种动态子群体变分自编码框架，联合实现表征学习与自适应OOD检测。该模型通过动态更新潜在结构，捕捉新出现的数据趋势。其基于嵌入相似性的非参数聚类机制，受高斯混合模型启发，可发现并建模潜在子群体。大量实验表明，DynaSubVAE在近域和远域检测任务中表现优异，在训练时完全缺失某一类的情况下仍具强鲁棒性。进一步验证显示，其动态子群体机制在OOD准确率与后悔精度上优于独立使用的GMM与KMeans++。
原文摘要 · Abstract (English)
Real-world observational data often contain existing or emerging heterogeneous subpopulations that deviate from global patterns. The majority of models tend to overlook these underrepresented groups, leading to inaccurate or even harmful predictions. Existing solutions often rely on detecting these samples as Out-of-domain (OOD) rather than adapting the model to new emerging patterns. We introduce DynaSubVAE, a Dynamic Subgrouping Variational Autoencoder framework that jointly performs representation learning and adaptive OOD detection. Unlike conventional approaches, DynaSubVAE evolves with the data by dynamically updating its latent structure to capture new trends. It leverages a novel non-parametric clustering mechanism, inspired by Gaussian Mixture Models, to discover and model latent subgroups based on embedding similarity. Extensive experiments show that DynaSubVAE achieves competitive performance in both near-OOD and far-OOD detection, and excels in class-OOD scenarios where an entire class is missing during training. We further illustrate that our dynamic subgrouping mechanism outperforms standalone clustering methods such as GMM and KMeans++ in terms of both OOD accuracy and regret precision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。