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Shenghua Fan

2 accepted papers

2026

Sparse Tuning Enhances Plasticity in PTM-based Continual Learning

AAAI 2026technical

Continual Learning with Pre-trained Models holds great promise for efficient adaptation across sequential tasks. However, most existing approaches freeze PTMs and rely on auxiliary modules like prompts or adapters, limiting model plasticity and leading to suboptimal generalization when facing signif

Cited by 0SourcePDFScholar
2026

Subspace Alignment for CLIP-based Continual Learning via Canonical Correlation Analysis

CVPR 2026

Recent advances in CLIP-based continual learning have shown the potential of leveraging pre-trained vision-language models for sequential tasks. However, existing methods overlook a key problem we call Asymmetric Drift. In unimodal CLIP-based continual learning, the visual branch undergoes stronger

Cited by 0SourcecodeScholar