Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning
<|reference_start|>Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning: Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Despite the strong performance of Pre-Trained Models (PTMs) in CIL, a critical issue persists: learning new classes often results in the overwriting of old ones. Excessive modification of the network causes forgetting, while minimal adjustments lead to an inadequate fit for new classes. As a result, it is desired to figure out a way of efficient model updating without harming former knowledge. In this paper, we propose ExpAndable Subspace Ensemble (EASE) for PTM-based CIL. To enable model updating without conflict, we train a distinct lightweight adapter module for each new task, aiming to create task-specific subspaces. These adapters span a high-dimensional feature space, enabling joint decision-making across multiple subspaces. As data evolves, the expanding subspaces render the old class classifiers incompatible with new-stage spaces. Correspondingly, we design a semantic-guided prototype complement strategy that synthesizes old classes' new features without using any old class instance. Extensive experiments on seven benchmark datasets verify EASE's state-of-the-art performance. Code is available at: https://github.com/sun-hailong/CVPR24-Ease<|reference_end|>
@article{zhou2024expandable,
    title={Expandable Subspace Ensemble for Pre-Trained Model-Based
  Class-Incremental Learning},
    author={Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, De-Chuan Zhan},
    journal={arXiv preprint arXiv:2403.12030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12030},
    primaryClass={cs.CV cs.LG}
}