Confidence Self-Calibration for Multi-Label Class-Incremental Learning

• URL: http://arxiv.org/abs/2403.12559v2
• Date: Mon, 12 Aug 2024 08:37:24 GMT
• Title: Confidence Self-Calibration for Multi-Label Class-Incremental Learning
• Authors: Kaile Du, Yifan Zhou, Fan Lyu, Yuyang Li, Chen Lu, Guangcan Liu,
• Abstract summary: Partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training. This issue leads to a proliferation of false-positive errors due to erroneously high confidence multi-label predictions. We propose a Confidence Self-Calibration (CSC) approach to refine multi-label confidence calibration in MLCIL.
• Score: 21.104984143597882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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