A semi-supervised semantic segmentation method combining aleatoric uncertainty loss and energy-based loss with union-intersection pseudo-labels gives modest gains over the CPCL baseline on PASCAL VOC and Cityscapes, but not state-of-the-art results.
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
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abstract
Understanding unsupervised domain adaptation has been an important task that has been well explored. However, the wide variety of methods have not analyzed the role of a classifier's performance in detail. In this paper, we thoroughly examine the role of a classifier in terms of matching source and target distributions. We specifically investigate the classifier ability by matching a) the distribution of features, b) probabilistic uncertainty for samples and c) certainty activation mappings. Our analysis suggests that using these three distributions does result in a consistently improved performance on all the datasets. Our work thus extends present knowledge on the role of the various distributions obtained from the classifier towards solving unsupervised domain adaptation.
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Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation
A semi-supervised semantic segmentation method combining aleatoric uncertainty loss and energy-based loss with union-intersection pseudo-labels gives modest gains over the CPCL baseline on PASCAL VOC and Cityscapes, but not state-of-the-art results.