paper-with-me

Multi-Task Learning 벤치마크

Multi-Task Learning on Cityscapes test

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mIoU

66.63 69.07 71.52 73.97 76.41 2018-10 2026-11 MultiObjectiveOptimization — 66.63 (2018-10-10) MultiObjectiveOptimization — 66.63 (2018-10-10) Nash-MTL — 75.41 (2022-02-02) Nash-MTL — 75.41 (2022-02-02) SwinMTL — 76.41 (2024-03-15) SwinMTL — 76.41 (2024-03-15) MultiObjectiveOptimization — 66.63 (2018-10-10) Nash-MTL — 75.41 (2022-02-02) SwinMTL — 76.41 (2024-03-15)
RankModel mIoURMSE PaperCodeYear
1 SwinMTL 76.410.51 SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images pardistaghavi/swinmtl 2024
2 Nash-MTL 75.41– Multi-Task Learning as a Bargaining Game torchjd/torchjd · avivnavon/nash-mtl · cranial-xix/famo · +1 2022
3 MultiObjectiveOptimization 66.63– Multi-Task Learning as Multi-Objective Optimization IntelVCL/MultiObjectiveOptimization · isl-org/multiobjectiveoptimization · ebagdasa/backdoors101 · +4 2018
4 SwinMTL 76.410.51 SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images pardistaghavi/swinmtl 2024
5 Nash-MTL 75.41– Multi-Task Learning as a Bargaining Game torchjd/torchjd · avivnavon/nash-mtl · cranial-xix/famo · +1 2022
6 MultiObjectiveOptimization 66.63– Multi-Task Learning as Multi-Objective Optimization IntelVCL/MultiObjectiveOptimization · isl-org/multiobjectiveoptimization · ebagdasa/backdoors101 · +4 2018
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