All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation Liyao T ang

This approach may, however, hinder the comprehensive exploitation of unlabeled data points. We hypothesize that this selective usage arises from the noise in pseudo-labels generated on unlabeled data. The noise in pseudo-labels may result in significant discrepancies between pseudo-labels and model predictions, thus confusing and affecting the model training greatly.