{"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/deep-hashing-network-for-unsupervised-domain","title":"Deep Hashing Network for Unsupervised Domain Adaptation","arxiv_id":"1706.07522","date":"2017-06-22","proceeding":"CVPR 2017 7","authors":["Hemanth Venkateswara","Jose Eusebio","Shayok Chakraborty","Sethuraman Panchanathan"],"abstract":"In recent years, deep neural networks have emerged as a dominant machine\nlearning tool for a wide variety of application domains. However, training a\ndeep neural network requires a large amount of labeled data, which is an\nexpensive process in terms of time, labor and human expertise. Domain\nadaptation or transfer learning algorithms address this challenge by leveraging\nlabeled data in a different, but related source domain, to develop a model for\nthe target domain. Further, the explosive growth of digital data has posed a\nfundamental challenge concerning its storage and retrieval. Due to its storage\nand retrieval efficiency, recent years have witnessed a wide application of\nhashing in a variety of computer vision applications. In this paper, we first\nintroduce a new dataset, Office-Home, to evaluate domain adaptation algorithms.\nThe dataset contains images of a variety of everyday objects from multiple\ndomains. We then propose a novel deep learning framework that can exploit\nlabeled source data and unlabeled target data to learn informative hash codes,\nto accurately classify unseen target data. To the best of our knowledge, this\nis the first research effort to exploit the feature learning capabilities of\ndeep neural networks to learn representative hash codes to address the domain\nadaptation problem. Our extensive empirical studies on multiple transfer tasks\ncorroborate the usefulness of the framework in learning efficient hash codes\nwhich outperform existing competitive baselines for unsupervised domain\nadaptation.","url_abs":"http://arxiv.org/abs/1706.07522v1","url_pdf":"http://arxiv.org/pdf/1706.07522v1.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":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/hemanthdv/da-hash","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/Evgeneus/Graph-Domain-Adaptaion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/deeplearning-wisc/hypo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/facebookresearch/DomainBed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/facebookresearch/ModelRatatouille","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/szubing/uniood","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-hashing-network-for-unsupervised-domain","repo_url":"https://github.com/xch-liu/geom-tex-dg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-hashing","task_name":"Deep Hashing"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[{"slug":"office-home","name":"Office-Home","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.07522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.07522"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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