Filtering out point-cloud samples with high saliency-map skewness from self-supervision losses raises point-cloud unsupervised domain adaptation accuracy by about 1 to 2 points on PointDA-10 and PointSegDA.
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Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness
Filtering out point-cloud samples with high saliency-map skewness from self-supervision losses raises point-cloud unsupervised domain adaptation accuracy by about 1 to 2 points on PointDA-10 and PointSegDA.