| An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale A Dosovitskiy*, L Beyer*, A Kolesnikov*, D Weissenborn*, X Zhai*, ... International Conference on Learning Representations (ICLR), 2021 | 16278 | 2021 |
| MLP-Mixer: An all-MLP Architecture for Vision I Tolstikhin*, N Houlsby*, A Kolesnikov*, L Beyer*, X Zhai, T Unterthiner, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 1149 | 2021 |
| Big Transfer (BiT): General Visual Representation Learning A Kolesnikov*, L Beyer*, X Zhai*, J Puigcerver, J Yung, S Gelly, ... European Conference on Computer Vision (ECCV), 2020 | 926* | 2020 |
| An image is worth 16x16 words: Transformers for image recognition at scale. arXiv 2020 A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2010 | 858 | 2010 |
| S4l: Self-supervised semi-supervised learning X Zhai*, A Oliver*, A Kolesnikov*, L Beyer*, *equal contribution International Conference on Computer Vision (ICCV), 1476-1485, 2019 | 743 | 2019 |
| Revisiting Self-Supervised Visual Representation Learning A Kolesnikov*, X Zhai*, L Beyer*, *equal contribution Computer Vision and Pattern Recognition (CVPR), 2019 | 689 | 2019 |
| Underspecification Presents Challenges for Credibility in Modern Machine Learning A D'Amour*, K Heller*, D Moldovan*, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research (JMLR), 2020 | 466 | 2020 |
| Scaling Vision Transformers X Zhai*, A Kolesnikov*, N Houlsby, L Beyer*, *equal contribution Computer Vision and Pattern Recognition (CVPR), 2022 | 449 | 2022 |
| Self-Supervised GANs via Auxiliary Rotation Loss T Chen, X Zhai, M Ritter, M Lucic, N Houlsby Computer Vision and Pattern Recognition (CVPR), 12154-12163, 2019 | 287 | 2019 |
| Learning cross-media joint representation with sparse and semisupervised regularization X Zhai, Y Peng, J Xiao IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 24 (6 …, 2013 | 261 | 2013 |
| How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers A Steiner*, A Kolesnikov*, X Zhai*, R Wightman, J Uszkoreit, L Beyer*, ... Transactions on Machine Learning Research (TMLR), 2022 | 259 | 2022 |
| Are we done with ImageNet? L Beyer*, OJ Hénaff*, A Kolesnikov*, X Zhai*, A Oord*, *equal contribution arXiv preprint arXiv:2006.07159, 2020 | 226 | 2020 |
| A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark X Zhai*, J Puigcerver*, A Kolesnikov*, P Ruyssen, C Riquelme, M Lucic, ... arXiv preprint arXiv:1910.04867, 2019 | 207* | 2019 |
| A large-scale study on regularization and normalization in GANs K Kurach, M Lučić, X Zhai, M Michalski, S Gelly International conference on machine learning, 3581-3590, 2019 | 173 | 2019 |
| LiT: Zero-Shot Transfer with Locked-image Text Tuning X Zhai*, X Wang*, B Mustafa*, A Steiner*, D Keysers, A Kolesnikov, ... Computer Vision and Pattern Recognition (CVPR), 2022 | 167 | 2022 |
| The gan landscape: Losses, architectures, regularization, and normalization K Kurach, M Lucic, X Zhai, M Michalski, S Gelly International Conference on Machine Learning (ICML), 2019 | 149 | 2019 |
| High-Fidelity Image Generation With Fewer Labels M Lučić, M Tschannen, M Ritter, X Zhai, O Bachem, S Gelly International Conference on Machine Learning (ICML), 4183-4192, 2019 | 142 | 2019 |
| Semi-supervised cross-media feature learning with unified patch graph regularization Y Peng, X Zhai, Y Zhao, X Huang IEEE transactions on circuits and systems for video technology (TCSVT) 26 (3 …, 2015 | 138 | 2015 |
| Heterogeneous metric learning with joint graph regularization for cross-media retrieval X Zhai, Y Peng, J Xiao Proceedings of the AAAI Conference on Artificial Intelligence 27 (1), 1198-1204, 2013 | 136 | 2013 |
| Revisiting the Calibration of Modern Neural Networks M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 125 | 2021 |
Lucas BeyerGoogle Brain, RWTH AachenBestätigte E-Mail-Adresse bei google.com
