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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_38_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_39_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_40_1","volume-title":"Interpolation consistency training for semi-supervised learning. arXiv preprint arXiv:1903.03825","author":"Verma Vikas","year":"2019","unstructured":"Vikas Verma , Kenji Kawaguchi , Alex Lamb , Juho Kannala , Yoshua Bengio , and David Lopez-Paz . 2019. Interpolation consistency training for semi-supervised learning. arXiv preprint arXiv:1903.03825 ( 2019 ). Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz. 2019. Interpolation consistency training for semi-supervised learning. arXiv preprint arXiv:1903.03825 (2019)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00542"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00637"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00718"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00261"},{"key":"e_1_3_2_1_47_1","volume-title":"Improving semantic segmentation via self-training. arXiv preprint arXiv:2004.14960","author":"Zhu Yi","year":"2020","unstructured":"Yi Zhu , Zhongyue Zhang , Chongruo Wu , Zhi Zhang , Tong He , Hang Zhang , R Manmatha , Mu Li , and Alexander Smola . 2020. 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