Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
2021
Semi-supervised learning aims to leverage a large amount of unlabeled data
for performance boosting. Existing works primarily focus on image
classification. In this paper, we delve into semi-supervised learning for
object detection, where labeled data are more labor-intensive to collect.
Current methods are easily distracted by noisy regions generated by pseudo
labels. To combat the noisy labeling, we propose noise-resistant
semi-supervised learning by quantifying the region uncertainty. We first
investigate the adverse effects brought by different forms of noise associated
with pseudo labels. Then we propose to quantify the uncertainty of regions by
identifying the noise-resistant properties of regions over different strengths.
By importing the region uncertainty quantification and promoting multipeak
probability distribution output, we introduce uncertainty into training and
further achieve noise-resistant learning. Experiments on both PASCAL VOC and MS
COCO demonstrate the extraordinary performance of our method.
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