HC-SOINN with STAR captures topological manifold structure in class features and aligns it to non-linear drift, improving over point-wise NCM when integrated into existing CIL methods.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
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HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.
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Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning
HC-SOINN with STAR captures topological manifold structure in class features and aligns it to non-linear drift, improving over point-wise NCM when integrated into existing CIL methods.
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HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.