{"url":"/method/lovasz-softmax","slug":"lovasz-softmax","name":"Lovasz-Softmax","full_name":"Lovasz-Softmax","full_name_withheld":false,"description_markdown":"The **Lovasz-Softmax loss** is a loss function for multiclass semantic segmentation that incorporates the [softmax](https://paperswithcode.com/method/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.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":"https://github.com/bermanmaxim/LovaszSoftmax/blob/7d48792d35a04d3167de488dd00daabbccd8334b/pytorch/lovasz_losses.py#L153","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Loss Functions","url":"/methods/category/loss-functions","pwc_aliases":[]}],"n_papers_tagged":5,"archive_num_papers":5,"papers_newest_first":[{"paper":"/paper/adversarial-multiscale-feature-learning-for","title":"Adversarial Multiscale Feature Learning for Overlapping Chromosome Segmentation","date":"2020-12-22","arxiv_id":"2012.11847","n_code_links":1,"syntology":null},{"paper":null,"title":"Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index","date":"2020-10-26","arxiv_id":"2010.13499","n_code_links":0,"syntology":null},{"paper":"/paper/tornado-net-multiview-total-variation","title":"TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module","date":"2020-08-24","arxiv_id":"2008.10544","n_code_links":0,"syntology":null},{"paper":"/paper/salsanext-fast-semantic-segmentation-of-lidar","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","date":"2020-03-07","arxiv_id":"2003.03653","n_code_links":5,"syntology":{"ran":7,"of":11,"unverified":4,"pointer_only":1}},{"paper":"/paper/the-lovasz-softmax-loss-a-tractable-surrogate-1","title":"The LovÃ¡sz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks","date":"2018-06-01","arxiv_id":null,"n_code_links":2,"syntology":null}],"papers_shown":5,"tasks":[{"task":"/task/segmentation","name":"Segmentation","papers":4},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":4},{"task":"/task/3d-semantic-segmentation","name":"3D Semantic Segmentation","papers":2},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":2},{"task":"/task/decoder","name":"Decoder","papers":2},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":2},{"task":null,"name":"Generative Adversarial Network","papers":1},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":1},{"task":"/task/robust-3d-semantic-segmentation","name":"Robust 3D Semantic Segmentation","papers":1},{"task":"/task/scene-understanding","name":"Scene Understanding","papers":1}],"tasks_shown":10,"n_tasks":10,"usage_by_year":[{"year":"2018","papers":1},{"year":"2020","papers":4}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/lovasz-softmax"},"syntology_read_at":"2026-09-25T09:33:49+00:00"}