CoMemNet is a dual-branch continual learning model for dynamic traffic networks that combines contrastive sampling via Wasserstein features and memory replay to achieve SOTA performance while mitigating forgetting.
Stwave ++: A multi- scale efficient spectral graph attention network with long-term trends for disentangled traffic flow forecasting,
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CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
CoMemNet is a dual-branch continual learning model for dynamic traffic networks that combines contrastive sampling via Wasserstein features and memory replay to achieve SOTA performance while mitigating forgetting.