{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/achievement-based-training-progress-balancing","title":"Achievement-Based Training Progress Balancing for Multi-Task Learning","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Hayoung Yun","Hanjoo Cho"],"abstract":"    Multi-task learning faces two challenging issues: (1) the high cost of annotating labels for all tasks and (2) balancing the training progress of various tasks with different natures. To resolve the label annotation issue, we construct a large-scale \"partially annotated\" multi-task dataset by combining task-specific datasets. However, the numbers of annotations for individual tasks are imbalanced, which may escalate an imbalance in training progress. To balance the training progress, we propose an achievement-based multi-task loss to modulate training speed based on the \"achievement,\" defined as the ratio of current accuracy to single-task accuracy. Then, we formulate the multitask loss as a weighted geometric mean of individual task losses instead of a weighted sum to prevent any task from dominating the loss. In experiments, we evaluated the accuracy and training speed of the proposed multi-task loss on the large-scale multi-task dataset against recent multitask losses. The proposed loss achieved the best multi-task accuracy without incurring training time overhead. Compared to single-task models, the proposed one achieved 1.28%, 1.65%, and 1.18% accuracy improvement in object detection, semantic segmentation, and depth estimation, respectively, while reducing computations to 33.73%. Source code is available at https://github.com/ samsung/Achievement-based-MTL.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Yun_Achievement-Based_Training_Progress_Balancing_for_Multi-Task_Learning_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Yun_Achievement-Based_Training_Progress_Balancing_for_Multi-Task_Learning_ICCV_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"achievement-based-training-progress-balancing","repo_url":"https://github.com/samsung/achievement-based-mtl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}