Universal Semi-Supervised Semantic Segmentation

In recent years, the need for semantic segmentation has arisen across several different applications and environments. However, the expense and redundancy of annotation often limits the quantity of labels available for training in any domain, while deployment is easier if a single model works well across domains... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization