| Meta-sgd: Learning to learn quickly for few-shot learning Z Li, F Zhou, F Chen, H Li arXiv preprint arXiv:1707.09835, 2017 | 1224 | 2017 |
| Deep meta-learning: Learning to learn in the concept space F Zhou, B Wu, Z Li arXiv preprint arXiv:1802.03596, 2018 | 154 | 2018 |
| Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization N Ye, K Li, H Bai, R Yu, L Hong, F Zhou, Z Li, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 71 | 2022 |
| Ood-bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms N Ye, K Li, L Hong, H Bai, Y Chen, F Zhou, Z Li arXiv preprint arXiv:2106.03721 1 (3), 5, 2021 | 58 | 2021 |
| Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation H Bai, R Sun, L Hong, F Zhou, N Ye, HJ Ye, SHG Chan, Z Li Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6705-6713, 2021 | 55 | 2021 |
| Meta-sgd: Learning to learn quickly for few-shot learning. arXiv 2017 Z Li, F Zhou, F Chen, H Li arXiv preprint arXiv:1707.09835, 2017 | 45 | 2017 |
| Nas-ood: Neural architecture search for out-of-distribution generalization H Bai, F Zhou, L Hong, N Ye, SHG Chan, Z Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 40 | 2021 |
| Adversarial robustness for unsupervised domain adaptation M Awais, F Zhou, H Xu, L Hong, P Luo, SH Bae, Z Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 38 | 2021 |
| Meta-sgd: Learning to learn quickly for few-shot learning. arXiv Z Li, F Zhou, F Chen, H Li arXiv preprint arXiv:1707.09835, 2017 | 31 | 2017 |
| Metaaugment: Sample-aware data augmentation policy learning F Zhou, J Li, C Xie, F Chen, L Hong, R Sun, Z Li Proceedings of the AAAI conference on artificial intelligence 35 (12), 11097 …, 2021 | 30 | 2021 |
| Mixacm: Mixup-based robustness transfer via distillation of activated channel maps A Muhammad, F Zhou, C Xie, J Li, SH Bae, Z Li Advances in Neural Information Processing Systems 34, 4555-4569, 2021 | 16 | 2021 |
| Your contrastive learning is secretly doing stochastic neighbor embedding T Hu, Z Liu, F Zhou, W Wang, W Huang arXiv preprint arXiv:2205.14814, 2022 | 15 | 2022 |
| Vega: towards an end-to-end configurable automl pipeline B Wang, H Xu, J Zhang, C Chen, X Fang, Y Xu, N Kang, L Hong, C Jiang, ... arXiv preprint arXiv:2011.01507, 2020 | 14 | 2020 |
| Autohash: Learning higher-order feature interactions for deep ctr prediction N Xue, B Liu, H Guo, R Tang, F Zhou, S Zafeiriou, Y Zhang, J Wang, Z Li IEEE Transactions on Knowledge and Data Engineering 34 (6), 2653-2666, 2020 | 14 | 2020 |
| Multi-objective neural architecture search via non-stationary policy gradient Z Chen, F Zhou, G Trimponias, Z Li arXiv preprint arXiv:2001.08437, 2020 | 13 | 2020 |
| Zood: Exploiting model zoo for out-of-distribution generalization Q Dong, A Muhammad, F Zhou, C Xie, T Hu, Y Yang, SH Bae, Z Li Advances in Neural Information Processing Systems 35, 31583-31598, 2022 | 11 | 2022 |
| Explore and exploit the diverse knowledge in model zoo for domain generalization Y Chen, T Hu, F Zhou, Z Li, ZM Ma International Conference on Machine Learning, 4623-4640, 2023 | 8 | 2023 |
| Dha: End-to-end joint optimization of data augmentation policy, hyper-parameter and architecture K Zhou, L Hong, S Hu, F Zhou, B Ru, J Feng, Z Li arXiv preprint arXiv:2109.05765, 2021 | 8 | 2021 |
| Formulating camera-adaptive color constancy as a few-shot meta-learning problem S McDonagh, S Parisot, F Zhou, X Zhang, A Leonardis, Z Li, G Slabaugh arXiv preprint arXiv:1811.11788, 2018 | 7 | 2018 |
| Binxin Ru, Jiashi Feng, and Zhenguo Li. Dha: End-to-end joint optimization of data augmentation policy, hyper-parameter and architecture K Zhou, L Hong, S Hu, F Zhou arXiv preprint arXiv:2109.05765 2 (6), 8, 2021 | 5 | 2021 |
