Multi-task learning on partially labeled datasets via invariant/equivariant semi-supervised learning

In three linesInvestigation of invariant and equivariant semi-supervised learning (FixMatch, Dense FixMatch) for multi-task training on partially labeled datasets. Evaluation on Cityscapes and BDD100K for object detection and semantic segmentation. Equivariant approaches outperform supervised baselines, especially with limited labeled samples per task.

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