{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:15:58Z","timestamp":1750220158238,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,29]]},"DOI":"10.1145\/3549179.3549180","type":"proceedings-article","created":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T22:07:10Z","timestamp":1661033230000},"page":"1-7","source":"Crossref","is-referenced-by-count":2,"title":["Image Data Augmentation Method based on Style Transfer"],"prefix":"10.1145","author":[{"given":"Yanyan","family":"Wei","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, China"}]},{"given":"Chuwei","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, China"}]},{"given":"Hangyu","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, China"}]},{"given":"Zhilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"7th International Conference on Learning Representations, ICLR","author":"Geirhos R.","year":"2019","unstructured":"Geirhos R. , Rubisch P. , Michaelis C. , 2019 . ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 7th International Conference on Learning Representations, ICLR 2019. Geirhos R., Rubisch P., Michaelis C., 2019. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, 7th International Conference on Learning Representations, ICLR 2019."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1167\/16.12.326"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.272"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_3_2_1_5_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Dumoulin V.","year":"2017","unstructured":"Dumoulin V. , Shlens J. , Kudlur M. 2017 . A Learned Representation For Artistic Style , 5th International Conference on Learning Representations, ICLR 2017. Dumoulin V., Shlens J., Kudlur M. 2017. A Learned Representation For Artistic Style, 5th International Conference on Learning Representations, ICLR 2017."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"e_1_3_2_1_8_1","unstructured":"DeVries T. Taylor G. W. 2017. Improved regularization of convolutional neural networks with cutout. arXiv preprint.arXiv:1708.04552  DeVries T. Taylor G. W. 2017. Improved regularization of convolutional neural networks with cutout. arXiv preprint.arXiv:1708.04552"},{"key":"e_1_3_2_1_9_1","volume-title":"6th International Conference on Learning Representations. ICLR","author":"Zhang H.","year":"2018","unstructured":"Zhang H. , Cisse M. , Dauphin Y. N. , 2018 . mixup: Beyond Empirical Risk Minimization , 6th International Conference on Learning Representations. ICLR 2018. Zhang H., Cisse M., Dauphin Y. N., 2018. mixup: Beyond Empirical Risk Minimization, 6th International Conference on Learning Representations. ICLR 2018."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"e_1_3_2_1_11_1","volume-title":"Liao H Y M","author":"Bochkovskiy A","year":"2020","unstructured":"Bochkovskiy A , Wang C Y , Liao H Y M . 2020 . Yolov4: Optimal speed and accuracy of object detection. arXiv preprint. arXiv:2004.10934 Bochkovskiy A, Wang C Y, Liao H Y M. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint. arXiv:2004.10934"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1962.1057698"},{"key":"e_1_3_2_1_13_1","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow I. J.","year":"2014","unstructured":"Goodfellow I. J. , Pouget-Abadie J. , Mirza M. , 2014 . Generative Adversarial Networks , Advances in Neural Information Processing Systems. Vol. 3. 2672 - 2680 Goodfellow I. J., Pouget-Abadie J., Mirza M., 2014. Generative Adversarial Networks, Advances in Neural Information Processing Systems. Vol. 3. 2672-2680","journal-title":"Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_1_16_1","volume-title":"4th International Conference on Learning Representations. ICLR","author":"Radford A.","year":"2016","unstructured":"Radford A. , Metz L. , Chintala S.. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks . 4th International Conference on Learning Representations. ICLR 2016 . Radford A., Metz L., Chintala S..2016. Unsupervised representation learning with deep convolutional generative adversarial networks. 4th International Conference on Learning Representations. ICLR 2016."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107404"},{"key":"e_1_3_2_1_19_1","volume-title":"3th International Conference on Learning Representations. ICLR","author":"Simonyan K.","year":"2015","unstructured":"Simonyan K. , Zisserman A. 2015 . Very deep convolutional networks for large-scale image recognition . 3th International Conference on Learning Representations. ICLR 2015. Simonyan K., Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. 3th International Conference on Learning Representations. ICLR 2015."},{"volume-title":"Microsoft COCO: Common Objects in Context","author":"Lin T. Y.","key":"e_1_3_2_1_20_1","unstructured":"Lin T. Y. , Maire M. , Belongie S. 2014. Microsoft COCO: Common Objects in Context . Springer International Publishing . Lin T. Y., Maire M., Belongie S. 2014. Microsoft COCO: Common Objects in Context. Springer International Publishing."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/310"},{"key":"e_1_3_2_1_22_1","volume-title":"IEEE International Conference on Image Processing. IEEE. 1404-1408","author":"Kim Y.","year":"2015","unstructured":"Kim Y. , Koh Y. J. , Lee C. , 2015 . Dark image enhancement based on pairwise target contrast and multi-scale detail boosting . IEEE International Conference on Image Processing. IEEE. 1404-1408 Kim Y., Koh Y. J., Lee C., 2015. Dark image enhancement based on pairwise target contrast and multi-scale detail boosting. IEEE International Conference on Image Processing. IEEE. 1404-1408"},{"key":"e_1_3_2_1_23_1","unstructured":"YOLOv5 from https:\/\/github.com\/ultralytics\/yolov5  YOLOv5 from https:\/\/github.com\/ultralytics\/yolov5"}],"event":{"name":"PRIS 2022: 2022 4th International Conference on Pattern Recognition and Intelligent Systems","acronym":"PRIS 2022","location":"Wuhan China"},"container-title":["2022 4th International Conference on Pattern Recognition and Intelligent Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549179.3549180","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3549179.3549180","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:22Z","timestamp":1750186822000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549179.3549180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":23,"alternative-id":["10.1145\/3549179.3549180","10.1145\/3549179"],"URL":"https:\/\/doi.org\/10.1145\/3549179.3549180","relation":{},"subject":[],"published":{"date-parts":[[2022,7,29]]}}}