Confidence Self-Calibration for Multi-Label Class-Incremental Learning
<|reference_start|>Confidence Self-Calibration for Multi-Label Class-Incremental Learning: The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable. This issue leads to a proliferation of false-positive errors due to erroneously high confidence multi-label predictions, exacerbating catastrophic forgetting within the disjoint label space. In this paper, we aim to refine multi-label confidence calibration in MLCIL and propose a Confidence Self-Calibration (CSC) approach. Firstly, for label relationship calibration, we introduce a class-incremental graph convolutional network that bridges the isolated label spaces by constructing learnable, dynamically extended label relationship graph. Then, for confidence calibration, we present a max-entropy regularization for each multi-label increment, facilitating confidence self-calibration through the penalization of over-confident output distributions. Our approach attains new state-of-the-art results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the calibration of label confidences confirmed through our methodology.<|reference_end|>
@article{du2024confidence,
    title={Confidence Self-Calibration for Multi-Label Class-Incremental Learning},
    author={Kaile Du, Yifan Zhou, Fan Lyu, Yuyang Li, Chen Lu, Guangcan Liu},
    journal={arXiv preprint arXiv:2403.12559},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12559},
    primaryClass={cs.CV cs.LG}
}