<ul data-eligibleForWebStory="true"><li>Catastrophic forgetting is a challenge faced in cloud-edge object detection for traffic monitoring due to the loss of previously learned knowledge when adapting to new data distributions.</li><li>Existing approaches like experience replay and visual prompts struggle to effectively prioritize historical data for optimal knowledge retention and adaptation.</li><li>A new algorithm called ER-EMU is proposed to mitigate catastrophic forgetting by using adaptive experience replay and a novel Domain Distance Metric-based Experience Selection (DDM-ES) algorithm.</li><li>Experiments on the Bellevue traffic video dataset show that ER-EMU consistently enhances the performance of cloud-edge object detection frameworks in dynamic traffic environments.</li></ul>

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay

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