Stebėti
Fengwei Zhou
Pavadinimas
Cituota
Cituota
Metai
Meta-sgd: Learning to learn quickly for few-shot learning
Z Li, F Zhou, F Chen, H Li
arXiv preprint arXiv:1707.09835, 2017
12272017
Deep meta-learning: Learning to learn in the concept space
F Zhou, B Wu, Z Li
arXiv preprint arXiv:1802.03596, 2018
1542018
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
722022
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
582021
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
552021
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
452017
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
402021
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
382021
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
312017
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
302021
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
162021
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
152022
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
142020
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
142020
Multi-objective neural architecture search via non-stationary policy gradient
Z Chen, F Zhou, G Trimponias, Z Li
arXiv preprint arXiv:2001.08437, 2020
132020
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
112022
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
82023
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
82021
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
72018
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
52021
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