  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.
