Jan 25, 2023 · We propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain ...
Feb 1, 2023 · This work proposes a novel framework for achieving target domain generalization, target domain adaptation, and forgetting compensation at the same time.
Official Implementation for ICLR 2023 paper: Deja Vu: Continual Model Generalization for Unseen Domains · Overview RaTP first starts with a labeled source ...
The framework includes a training- free data augmentation module that generates more data for improving model's generalization ability, a new pseudo-labeling ...
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Domain Generalization (DG) aims to improve the generalization ability of models trained on a specific group of source domains, enabling them to perform well on ...
Official Implementation for ICLR 2023 paper: Deja Vu: Continual Model Generalization for Unseen Domains. The code is released at a new repository.
Mar 10, 2023 · This Appendix includes additional details for the ICLR 2023 paper“Deja Vu: Continual Model. Generalization for Unseen Domains”, including ...
Mar 30, 2023 · In our team's forthcoming ICLR 2023 paper “Deja Vu: Continual Model Generalization for Unseen Domains,” they achieve multiple goals in continual ...
Explore all code implementations available for DEJA VU: Continual Model Generalization For Unseen Domains.