{"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/modified-distribution-alignment-for-domain","title":"Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet","arxiv_id":"1904.02322","date":"2019-04-04","proceeding":null,"authors":["Youshan Zhang","Brian D. Davison"],"abstract":"Deep neural networks have been widely used in computer vision. There are\nseveral well trained deep neural networks for the ImageNet classification\nchallenge, which has played a significant role in image recognition. However,\nlittle work has explored pre-trained neural networks for image recognition in\ndomain adaption. In this paper, we are the first to extract better-represented\nfeatures from a pre-trained Inception ResNet model for domain adaptation. We\nthen present a modified distribution alignment method for classification using\nthe extracted features. We test our model using three benchmark datasets\n(Office+Caltech-10, Office-31, and Office-Home). Extensive experiments\ndemonstrate significant improvements (4.8%, 5.5%, and 10%) in classification\naccuracy over the state-of-the-art.","url_abs":"http://arxiv.org/abs/1904.02322v2","url_pdf":"http://arxiv.org/pdf/1904.02322v2.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":"modified-distribution-alignment-for-domain","repo_url":"https://github.com/heaventian93/MDAIR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"modified-distribution-alignment-for-domain","repo_url":"https://github.com/heaventian93/ImageNet-Models-on-Domain-Adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"classification","task_name":"General Classification"}],"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":[{"leaderboard":"/sota/domain-adaptation-on-office-31","task":"Domain Adaptation","dataset":"Office-31","model":"MDAIR","rank_in_archive_order":17,"of":40,"metrics":{"Average Accuracy":"89.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.02322","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}