Weakly supervised multi-label classification (WSML) task, which is to learn a
multi-label classification using partially observed labels per image, is
becoming increasingly important due to its huge annotation cost. In this work,
we first regard unobserved labels as negative labels, casting the WSML task
into noisy multi-label classification. From this point of view, we empirically
observe that memorization effect, which was first discovered in a noisy
multi-class setting, also occurs in a multi-label setting. That is, the model
first learns the representation of clean labels, and then starts memorizing
noisy labels. Based on this finding, we propose novel methods for WSML which
reject or correct the large loss samples to prevent model from memorizing the
noisy label. Without heavy and complex components, our proposed methods
outperform previous state-of-the-art WSML methods on several partial label
settings including Pascal VOC 2012, MS COCO, NUSWIDE, CUB, and OpenImages V3
datasets. Various analysis also show that our methodology actually works well,
validating that treating large loss properly matters in a weakly supervised
multi-label classification. Our code is available at
this https URL

本文探究弱监督多标签分类任务中的噪声与过拟合问题，并提出了能够处理大损失值样本的方法，得出准确性更高的预测结果。