Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels

• URL: http://arxiv.org/abs/2302.14695v1
• Date: Tue, 28 Feb 2023 16:08:12 GMT
• Title: Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
• Authors: Xiang Li, Xinrui Wang, Songcan Chen
• Abstract summary: In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. Existing Multi-Label Learning methods assume unknown labels as negatives, which introduces false negatives as noisy labels. We propose a more practical and cheaper alternative: Single Positive Multi-Label Learning (SPMLL), where only one positive label needs to be provided per sample.
• Score: 29.11589378265006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be Single Positive Multi-Label Learning (SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL methods usually assume unknown labels as negatives, which inevitably introduces false negatives as noisy labels. More seriously, Binary Cross Entropy (BCE) loss is often used for training, which is notoriously not robust to noisy labels. To mitigate this issue, we customize an objective function for SPMLL by pushing only one pair of labels apart each time to prevent the domination of negative labels, which is the main culprit of fitting noisy labels in SPMLL. To further combat such noisy labels, we explore the high-rankness of label matrix, which can also push apart different labels. By directly extending from SPMLL to MLL with full labels, a unified loss applicable to both settings is derived. Experiments on real datasets demonstrate that the proposed loss not only performs more robustly to noisy labels for SPMLL but also works well for full labels. Besides, we empirically discover that high-rankness can mitigate the dramatic performance drop in SPMLL. Most surprisingly, even without any regularization or fine-tuned label correction, only adopting our loss defeats state-of-the-art SPMLL methods on CUB, a dataset that severely lacks labels.

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