UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
Deep class-incremental learning: A survey
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C-Flat Turbo accelerates continual learning by skipping redundant flatness gradients via direction-invariance observations and linear adaptive scheduling, delivering 1-1.25x speedup with comparable accuracy.
SoTU merges sparse orthogonal delta parameters learned across streaming tasks to fuse knowledge and mitigate forgetting in pre-trained model continual learning.
citing papers explorer
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UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
UniAlign improves robustness of deep learning NTC models under distribution shifts via domain alignment fine-tuning and stable ensembling, yielding 2.51% accuracy and 2.71% F1 gains over standard training on three public datasets.
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A Faster Path to Continual Learning
C-Flat Turbo accelerates continual learning by skipping redundant flatness gradients via direction-invariance observations and linear adaptive scheduling, delivering 1-1.25x speedup with comparable accuracy.
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Sparse Orthogonal Parameters Tuning for Continual Learning
SoTU merges sparse orthogonal delta parameters learned across streaming tasks to fuse knowledge and mitigate forgetting in pre-trained model continual learning.