{"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/on-the-ideal-number-of-groups-for-isometric","title":"On the Ideal Number of Groups for Isometric Gradient Propagation","arxiv_id":"2302.03193","date":"2023-02-07","proceeding":null,"authors":["Bum Jun Kim","Hyeyeon Choi","Hyeonah Jang","Sang Woo Kim"],"abstract":"Recently, various normalization layers have been proposed to stabilize the training of deep neural networks. Among them, group normalization is a generalization of layer normalization and instance normalization by allowing a degree of freedom in the number of groups it uses. However, to determine the optimal number of groups, trial-and-error-based hyperparameter tuning is required, and such experiments are time-consuming. In this study, we discuss a reasonable method for setting the number of groups. First, we find that the number of groups influences the gradient behavior of the group normalization layer. Based on this observation, we derive the ideal number of groups, which calibrates the gradient scale to facilitate gradient descent optimization. Our proposed number of groups is theoretically grounded, architecture-aware, and can provide a proper value in a layer-wise manner for all layers. The proposed method exhibited improved performance over existing methods in numerous neural network architectures, tasks, and datasets.","url_abs":"https://arxiv.org/abs/2302.03193v1","url_pdf":"https://arxiv.org/pdf/2302.03193v1.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":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"}],"methods":[{"method_slug":"group-normalization","method_name":"Group Normalization"},{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-image-classification-on-caltech","task":"Fine-Grained Image Classification","dataset":"Caltech-101","model":"ResNet-101 (ideal number of groups)","rank_in_archive_order":15,"of":18,"metrics":{"Top-1 Error Rate":"22.247%"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-image-classification-on-oxford-2","task":"Fine-Grained Image Classification","dataset":"Oxford-IIIT Pets","model":"ResNet-101 (ideal number of groups)","rank_in_archive_order":18,"of":19,"metrics":{"Accuracy":"77.076"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-mnist","task":"Image Classification","dataset":"MNIST","model":"MLP (ideal number of groups)","rank_in_archive_order":53,"of":81,"metrics":{"Percentage error":"1.67"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-coco-2017","task":"Object Detection","dataset":"COCO 2017","model":"Faster R-CNN (ideal number of groups)","rank_in_archive_order":7,"of":24,"metrics":{"AP":"40.7","AP50":"61.2","AP75":"44.6"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-coco-panoptic","task":"Panoptic Segmentation","dataset":"COCO panoptic","model":"PFPN (ideal number of groups)","rank_in_archive_order":2,"of":2,"metrics":{"PQ":"42.147","PQst":"30.572","PQth":"49.816"},"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}