DRIFT is a benchmark modeling continual graph data streams as time-varying mixtures of latent task distributions via Gaussian parameterization, revealing substantial performance degradation in existing continual learning methods under task-free continuous drift.
Continual learning on dynamic graphs via parameter isolation
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HDSD decouples parameter subspaces in vision-language models via a Feature Modulation Module, General Fusion Module with adaptive thresholds, and Hierarchical Learning Module with SVD scaling to minimize cross-task interference and achieve state-of-the-art class-incremental learning performance.
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DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
DRIFT is a benchmark modeling continual graph data streams as time-varying mixtures of latent task distributions via Gaussian parameterization, revealing substantial performance degradation in existing continual learning methods under task-free continuous drift.
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Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Models
HDSD decouples parameter subspaces in vision-language models via a Feature Modulation Module, General Fusion Module with adaptive thresholds, and Hierarchical Learning Module with SVD scaling to minimize cross-task interference and achieve state-of-the-art class-incremental learning performance.