Deja vu: Continual model generalization for unseen domains

C Liu, L Wang, L Lyu, C Sun, X Wang, Q Zhu - arXiv preprint arXiv …, 2023 - arxiv.org
In real-world applications, deep learning models often run in non-stationary environments
where the target data distribution continually shifts over time. There have been numerous …
被引用次数：22 相关文章

DEJA VU: Continual Model Generalization For Unseen Domains

C Liu, L Wang, L Lyu, C Sun, X Wang, Q Zhu - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
In real-world applications, deep learning models often run in non-stationary environments
where the target data distribution continually shifts over time. There have been numerous …

Deja Vu: Continual Model Generalization for Unseen Domains

C Liu, L Wang, L Lyu, C Sun, X Wang… - … Conference on Learning …, 2022 - openreview.net
In real-world applications, deep learning models often run in non-stationary environments
where the target data distribution continually shifts over time. There have been numerous …
[PDF] researchgate.net

[PDF][PDF] DEJA VU: CONTINUAL MODEL GENERALIZATION FOR UNSEEN DOMAINS

C Liu, L Wang, L Lyu, C Sun, X Wang, Q Zhu - researchgate.net
In real-world applications, deep learning models often run in non-stationary environments
where the target data distribution continually shifts over time. There have been numerous …

[引用][C] Deja Vu: Continual Model Generalization for Unseen Domains

C Liu, L Wang, L Lyu, C Sun, X Wang, Q Zhu - 2023 - par.nsf.gov