{"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/the-gist-and-rist-of-iterative-self-training","title":"The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation","arxiv_id":"2103.17105","date":"2021-03-31","proceeding":null,"authors":["Eu Wern Teh","Terrance DeVries","Brendan Duke","Ruowei Jiang","Parham Aarabi","Graham W. Taylor"],"abstract":"We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples. We focus on iterative self-training methods in which we explore the behavior of self-training over multiple refinement stages. We show that iterative self-training leads to performance degradation if done na\\\"ively with a fixed ratio of human-labeled to pseudo-labeled training examples. We propose Greedy Iterative Self-Training (GIST) and Random Iterative Self-Training (RIST) strategies that alternate between training on either human-labeled data or pseudo-labeled data at each refinement stage, resulting in a performance boost rather than degradation. We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.","url_abs":"https://arxiv.org/abs/2103.17105v3","url_pdf":"https://arxiv.org/pdf/2103.17105v3.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":[],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-3","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 100 samples labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":8,"of":13,"metrics":{"Validation mIoU":"58.70%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-2","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 12.5% labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":29,"of":33,"metrics":{"Validation mIoU":"62.57%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-18","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 2% labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":1,"of":3,"metrics":{"Validation mIoU":"53.51%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-1","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 25% labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":26,"of":30,"metrics":{"Validation mIoU":"65.14%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-19","task":"Semi-Supervised Semantic Segmentation","dataset":"Cityscapes 5% labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":1,"of":3,"metrics":{"Validation mIoU":"59.98%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-4","task":"Semi-Supervised Semantic Segmentation","dataset":"Pascal VOC 2012 12.5% labeled","model":"GIST and RIST","rank_in_archive_order":32,"of":38,"metrics":{"Validation mIoU":"70.76%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-6","task":"Semi-Supervised Semantic Segmentation","dataset":"Pascal VOC 2012 2% labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":3,"of":12,"metrics":{"Validation mIoU":"67.21%"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-5","task":"Semi-Supervised Semantic Segmentation","dataset":"Pascal VOC 2012 5% labeled","model":"GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)","rank_in_archive_order":7,"of":14,"metrics":{"Validation mIoU":"69.40%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}