Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs

arXiv:2401.14387v2 Announce Type: replace
Abstract: Efficiently generating sufficient labeled data remains a major bottleneck in deep learning, particularly for image segmentation tasks where labeling requires significant time and effort. This study tackles this issue in a resource-constrained environment, devoid of extensive datasets or pre-existing models. We introduce Inconsistency Masks (IM), a novel approach that filters uncertainty in image-pseudo-label pairs to substantially enhance segmentation quality, surpassing traditional semi-supervised learning techniques. Employing IM, we achieve strong segmentation results with as little as …

arxiv cs.cv masks type uncertainty