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Hongcheng Li

2 accepted papers

2025

Adapt and Feature Translation for Class-Incremental Learning with Pre-Trained Models

ICASSP 2025accepted

Class-incremental learning (CIL) aims to enable a learning system to continuously learn new information. Although pre-trained models (PTMs) exhibit strong performance in CIL, the lack of data from previously learned classes results in class imbalance and catastrophic forgetting during the updating p…

Cited by 0SourceScholar
2025

Diversity-Enhanced Distribution Alignment for Dataset Distillation

ICCV 2025poster

Dataset distillation, which compresses large-scale datasets into compact synthetic representations (i.e., distilled datasets), has become crucial for the efficient training of modern deep learning architectures. While existing large-scale dataset distillation methods leverage a pre-trained model thr…

Cited by 0SourcePDFScholar