Semi-Supervised RGBD Semantic Segmentation
2 papers with code • 1 benchmarks • 1 datasets
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Most implemented papers
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Without changing the network architecture, Mean Teacher achieves an error rate of 4. 35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels.
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities.