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Lovasz-Softmax

5 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The Lovasz-Softmax loss is a loss function for multiclass semantic segmentation that incorporates the softmax operation in the Lovasz extension. The Lovasz extension is a means by which we can achieve direct optimization of the mean intersection-over-union loss in neural networks.

See Code · bermanmaxim/LovaszSoftmax

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

10 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation4
Semantic Segmentation4
3D Semantic Segmentation2
Autonomous Driving2
Decoder2
Image Segmentation2
Generative Adversarial Network1
Medical Image Segmentation1
Robust 3D Semantic Segmentation1
Scene Understanding1

Usage over time archive 2025-07-28

Papers per year tagged with Lovasz-Softmax: 2018 to 2020, peak 4 4 0 2018: 1 paper 2018 2019: 0 papers 2019 2020: 4 papers 2020
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Loss Functions

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