用共享特征+独立分支提升模型不确定性估计效率
Divergent Ensemble Networks: Enhancing Uncertainty Estimation with Shared Representations and Independent Branching
- 共享输入层提取共性特征，后接独立分支形成集成
- 参数冗余降低，同时保持各分支多样性
- 适合需要高效不确定性估计的场景
集成学习在提升神经网络预测性能和不确定性估计方面已证明有效。然而，传统集成方法因完全独立训练网络，常导致参数冗余和计算效率低下。为此，我们提出新型架构——发散集成网络（Divergent Ensemble Network, DEN），结合共享表示学习与独立分支结构。DEN采用共享输入层捕捉所有分支的共同特征，随后通过可独立训练的发散层构成集成。该共享到分支的结构在减少参数冗余的同时维持集成多样性，实现高效且可扩展的学习。
原文摘要 · Abstract (English)
Ensemble learning has proven effective in improving predictive performance and estimating uncertainty in neural networks. However, conventional ensemble methods often suffer from redundant parameter usage and computational inefficiencies due to entirely independent network training. To address these challenges, we propose the Divergent Ensemble Network (DEN), a novel architecture that combines shared representation learning with independent branching. DEN employs a shared input layer to capture common features across all branches, followed by divergent, independently trainable layers that form an ensemble. This shared-to-branching structure reduces parameter redundancy while maintaining ensemble diversity, enabling efficient and scalable learning.
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