Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering
本文提出一种通过早期让利益相关者参与工作原型，再逐步实现联合和互操作性的方法，以解决企业数字孪生工程中的信任问题。
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When Imbalance Comes Twice: Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation
This study addresses the degradation of active learning performance in binary semantic segmentation caused by the coexistence of class imbalance and label shift. For the first time, it systematically simulates both challenges jointly on open-source datasets to evaluate the effectiveness of three active learning strategies: random sampling, entropy maximization, and core-set selection. Experimental results demonstrate that entropy-based and core-set methods remain robust under severe class imbalance; however, strong label shift significantly impairs their performance. By revealing distinct behavioral patterns of these strategies under compound distribution shifts, this work provides critical insights for deploying active learning in real-world scenarios where multiple data biases may co-occur.
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Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering
本文提出一种通过早期让利益相关者参与工作原型，再逐步实现联合和互操作性的方法，以解决企业数字孪生工程中的信任问题。
0 citationsRead paper
When Imbalance Comes Twice: Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation
This study addresses the degradation of active learning performance in binary semantic segmentation caused by the coexistence of class imbalance and label shift. For the first time, it systematically simulates both challenges jointly on open-source datasets to evaluate the effectiveness of three active learning strategies: random sampling, entropy maximization, and core-set selection. Experimental results demonstrate that entropy-based and core-set methods remain robust under severe class imbalance; however, strong label shift significantly impairs their performance. By revealing distinct behavioral patterns of these strategies under compound distribution shifts, this work provides critical insights for deploying active learning in real-world scenarios where multiple data biases may co-occur.
0 citationsRead paper