{"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/res-se-net-boosting-performance-of-resnets-by","title":"RES-SE-NET: Boosting Performance of Resnets by Enhancing Bridge-connections","arxiv_id":"1902.06066","date":"2019-02-16","proceeding":null,"authors":["Varshaneya V","Balasubramanian S","Darshan Gera"],"abstract":"One of the ways to train deep neural networks effectively is to use residual\nconnections. Residual connections can be classified as being either identity\nconnections or bridge-connections with a reshaping convolution. Empirical\nobservations on CIFAR-10 and CIFAR-100 datasets using a baseline Resnet model,\nwith bridge-connections removed, have shown a significant reduction in\naccuracy. This reduction is due to lack of contribution, in the form of feature\nmaps, by the bridge-connections. Hence bridge-connections are vital for Resnet.\nHowever, all feature maps in the bridge-connections are considered to be\nequally important. In this work, an upgraded architecture \"Res-SE-Net\" is\nproposed to further strengthen the contribution from the bridge-connections by\nquantifying the importance of each feature map and weighting them accordingly\nusing Squeeze-and-Excitation (SE) block. It is demonstrated that Res-SE-Net\ngeneralizes much better than Resnet and SE-Resnet on the benchmark CIFAR-10 and\nCIFAR-100 datasets.","url_abs":"http://arxiv.org/abs/1902.06066v1","url_pdf":"http://arxiv.org/pdf/1902.06066v1.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":"res-se-net-boosting-performance-of-resnets-by","repo_url":"https://github.com/varshaneya/Res-SE-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"res-se-net-boosting-performance-of-resnets-by","repo_url":"https://github.com/2023-MindSpore-4/Code14/tree/main/SE-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"res-se-net-boosting-performance-of-resnets-by","repo_url":"https://github.com/2023-MindSpore-4/Code6/tree/main/SE-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"res-se-net-boosting-performance-of-resnets-by","repo_url":"https://github.com/MindSpore-paper-code-3/code5/tree/main/SE-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"res-se-net-boosting-performance-of-resnets-by","repo_url":"https://github.com/code-implementation1/Code8/tree/main/SE-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}