Domain Generalization Guided by Large-Scale Pre-Trained Priors
<|reference_start|>Domain Generalization Guided by Large-Scale Pre-Trained Priors: Domain generalization (DG) aims to train a model from limited source domains, allowing it to generalize to unknown target domains. Typically, DG models only employ large-scale pre-trained models during the initialization of fine-tuning. However, large-scale pre-trained models already possess the ability to resist domain shift. If we reference pre-trained models continuously during fine-tuning to maintain this ability, it could further enhance the generalization ability of the DG model. For this purpose, we introduce a new method called Fine-Tune with Large-scale pre-trained Priors (FT-LP), which incorporates the pre-trained model as a prior into the DG fine-tuning process, ensuring that the model refers to its pre-trained model at each optimization step. FT-LP comprises a theoretical framework and a simple implementation strategy. In theory, we verify the rationality of FT-LP by introducing a generalization error bound with the pre-trained priors for DG. In implementation, we utilize an encoder to simulate the model distribution, enabling the use of FT-LP when only pre-trained weights are available. In summary, we offer a new fine-tuning method for DG algorithms to utilize pre-trained models throughout the fine-tuning process. Through experiments on various datasets and DG models, our proposed method exhibits significant improvements, indicating its effectiveness.<|reference_end|>
@article{wang2024domain,
    title={Domain Generalization Guided by Large-Scale Pre-Trained Priors},
    author={Zongbin Wang, Bin Pan, Shiyu Shen, Tianyang Shi, Zhenwei Shi},
    journal={arXiv preprint arXiv:2406.05628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05628},
    primaryClass={cs.LG}
}