{"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/pick-up-the-pace-fast-and-simple-domain","title":"Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling","arxiv_id":"2205.13508","date":"2022-05-26","proceeding":null,"authors":["Christopher Liao","Theodoros Tsiligkaridis","Brian Kulis"],"abstract":"Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution labeled data. Most state-of-the-art DA algorithms require an extensive amount of hyperparameter tuning and are computationally intensive due to the large batch sizes required. In this work, we propose a fast and simple DA method consisting of three stages: (1) domain alignment by covariance matching, (2) pseudo-labeling, and (3) ensembling. We call this method $\\textbf{PACE}$, for $\\textbf{P}$seudo-labels, $\\textbf{A}$lignment of $\\textbf{C}$ovariances, and $\\textbf{E}$nsembles. PACE is trained on top of fixed features extracted from an ensemble of modern pretrained backbones. PACE exceeds previous state-of-the-art by $\\textbf{5 - 10 \\%}$ on most benchmark adaptation tasks without training a neural network. PACE reduces training time and hyperparameter tuning time by $82\\%$ and $97\\%$, respectively, when compared to state-of-the-art DA methods. Code is released here: https://github.com/Chris210634/PACE-Domain-Adaptation","url_abs":"https://arxiv.org/abs/2205.13508v1","url_pdf":"https://arxiv.org/pdf/2205.13508v1.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":"pick-up-the-pace-fast-and-simple-domain","repo_url":"https://github.com/chris210634/pace-domain-adaptation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.13508","atlas_url":"https://app.syntology.ai/?focus=2205.13508","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}