{"_id":"14105","citation":{"ieee":"S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, United States, 2023.","mla":"Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.” 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.","ista":"Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision.","ama":"Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers; 2023. doi:10.1109/wacv56688.2023.00278","apa":"Sinha, S., Gehler, P., Locatello, F., & Schiele, B. (2023). TeST: Test-time Self-Training under distribution shift. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa, HI, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/wacv56688.2023.00278","short":"S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023.","chicago":"Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.” In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers, 2023. https://doi.org/10.1109/wacv56688.2023.00278."},"publication":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision","doi":"10.1109/wacv56688.2023.00278","abstract":[{"lang":"eng","text":"Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline testtime adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4, 43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks:\r\nobject detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new stateof-the art for test-time domain adaptation algorithms. "}],"main_file_link":[{"url":"https://arxiv.org/abs/2209.11459","open_access":"1"}],"oa_version":"Preprint","quality_controlled":"1","title":"TeST: Test-time Self-Training under distribution shift","article_processing_charge":"No","date_published":"2023-02-06T00:00:00Z","extern":"1","publication_identifier":{"eissn":["2642-9381"],"isbn":["9781665493475"]},"type":"conference","day":"06","publication_status":"published","author":[{"full_name":"Sinha, Samarth","last_name":"Sinha","first_name":"Samarth"},{"first_name":"Peter","last_name":"Gehler","full_name":"Gehler, Peter"},{"orcid":"0000-0002-4850-0683","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"full_name":"Schiele, Bernt","last_name":"Schiele","first_name":"Bernt"}],"year":"2023","language":[{"iso":"eng"}],"department":[{"_id":"FrLo"}],"status":"public","month":"02","date_updated":"2023-09-06T10:26:56Z","date_created":"2023-08-21T12:11:38Z","conference":{"end_date":"2023-01-07","location":"Waikoloa, HI, United States","name":"WACV: Winter Conference on Applications of Computer Vision","start_date":"2023-01-02"},"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Institute of Electrical and Electronics Engineers","external_id":{"arxiv":["2209.11459"]},"scopus_import":"1"}