| Learning from simulated and unsupervised images through adversarial training A Shrivastava, T Pfister, O Tuzel, J Susskind, W Wang, R Webb Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2215 | 2017 |
| Temporal fusion transformers for interpretable multi-horizon time series forecasting B Lim, SÖ Arık, N Loeff, T Pfister International Journal of Forecasting 37 (4), 1748-1764, 2021 | 1284 | 2021 |
| Tabnet: Attentive interpretable tabular learning SÖ Arik, T Pfister Proceedings of the AAAI conference on artificial intelligence 35 (8), 6679-6687, 2021 | 1157 | 2021 |
| Cutpaste: Self-supervised learning for anomaly detection and localization CL Li, K Sohn, J Yoon, T Pfister Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 716 | 2021 |
| Flowing convnets for human pose estimation in videos T Pfister, J Charles, A Zisserman Proceedings of the IEEE international conference on computer vision, 1913-1921, 2015 | 694 | 2015 |
| A Spontaneous Micro-expression Database: Inducement, Collection and Baseline X Li, T Pfister, X Huang, G Zhao, M Pietikäinen Automatic Face and Gesture Recognition (FG), 2013 | 636 | 2013 |
| Recognising Spontaneous Facial Micro-expressions T Pfister, X Li, G Zhao, M Pietikäinen International Conference on Computer Vision (ICCV), 2011 | 516 | 2011 |
| Learning to prompt for continual learning Z Wang, Z Zhang, CY Lee, H Zhang, R Sun, X Ren, G Su, V Perot, J Dy, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 499 | 2022 |
| A Simple Semi-Supervised Learning Framework for Object Detection K Sohn, Z Zhang, CL Li, H Zhang, CY Lee, T Pfister arXiv preprint arXiv:2005.04757, 2020 | 484 | 2020 |
| Towards reading hidden emotions: A comparative study of spontaneous micro-expression spotting and recognition methods X Li, X Hong, A Moilanen, X Huang, T Pfister, G Zhao, M Pietikäinen IEEE transactions on affective computing 9 (4), 563-577, 2017 | 405 | 2017 |
| On completeness-aware concept-based explanations in deep neural networks CK Yeh, B Kim, S Arik, CL Li, T Pfister, P Ravikumar Advances in neural information processing systems 33, 20554-20565, 2020 | 320* | 2020 |
| Pseudoseg: Designing pseudo labels for semantic segmentation Y Zou, Z Zhang, H Zhang, CL Li, X Bian, JB Huang, T Pfister arXiv preprint arXiv:2010.09713, 2020 | 299 | 2020 |
| Dualprompt: Complementary prompting for rehearsal-free continual learning Z Wang, Z Zhang, S Ebrahimi, R Sun, H Zhang, CY Lee, X Ren, G Su, ... European Conference on Computer Vision, 631-648, 2022 | 294 | 2022 |
| Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes CY Hsieh, CL Li, CK Yeh, H Nakhost, Y Fujii, A Ratner, R Krishna, CY Lee, ... arXiv preprint arXiv:2305.02301, 2023 | 242 | 2023 |
| Learning and evaluating representations for deep one-class classification K Sohn, CL Li, J Yoon, M Jin, T Pfister arXiv preprint arXiv:2011.02578, 2020 | 222 | 2020 |
| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 198 | 2022 |
| Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos T Pfister, K Simonyan, J Charles, A Zisserman Asian Conference on Computer Vision (ACCV), 2014 | 195 | 2014 |
| Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost M Gao, Z Zhang, G Yu, SO Arik, LS Davis, T Pfister ECCV, 2020 | 190 | 2020 |
| Data Valuation using Reinforcement Learning J Yoon, SO Arik, T Pfister ICML, 2020 | 185 | 2020 |
| Distilling effective supervision from severe label noise Z Zhang, H Zhang, SO Arik, H Lee, T Pfister Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 147 | 2020 |
Sercan O. ArikGoogle在 google.com 的电子邮件经过验证
