Rep-MTL regularizes the shared representation space of multi-task networks by adding an entropy penalty on task saliency and a contrastive alignment between tasks, reporting competitive gains on NYUv2, Cityscapes, Office-31, and Office-Home.
Encoder-decoder with atrous separable convolution for semantic image segmentation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning
Rep-MTL regularizes the shared representation space of multi-task networks by adding an entropy penalty on task saliency and a contrastive alignment between tasks, reporting competitive gains on NYUv2, Cityscapes, Office-31, and Office-Home.