3 papers
cs.CV2025
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
Qiang Wang, Xiang Song, Yuhang He +4
Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time. Domain Incremental Learning (DIL) offers a solution by ena…
cs.CV2024
Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning
Xinyuan Gao, Songlin Dong, Yuhang He +2
The problem of Rehearsal-Free Continual Learning (RFCL) aims to continually learn new knowledge while preventing forgetting of the old knowledge, without storing any old samples an…
cs.CV2024
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
Xinyuan Gao, Songlin Dong, Yuhang He +2
In real-world applications, dynamic scenarios require the models to possess the capability to learn new tasks continuously without forgetting the old knowledge. Experience-Replay m…