ADaCoRe enables memory-bounded UICL for EEG by compressing and reconstructing signals while preserving key morphologies, outperforming baselines with gains of at least +2.7 and +15.3 ACC on ISRUC and FACED datasets.
Continual learning through synaptic intelligence
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
DRDN reports 77.19% average accuracy on CIFAR100-B0 (10 steps) in from-scratch ViT class-incremental learning by routing MIM reconstruction through the backbone and using per-layer task token expansion.
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Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning
ADaCoRe enables memory-bounded UICL for EEG by compressing and reconstructing signals while preserving key morphologies, outperforming baselines with gains of at least +2.7 and +15.3 ACC on ISRUC and FACED datasets.
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DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
DRDN reports 77.19% average accuracy on CIFAR100-B0 (10 steps) in from-scratch ViT class-incremental learning by routing MIM reconstruction through the backbone and using per-layer task token expansion.