@inproceedings{
wang2024dualstream,
title={Dual-stream Feature Augmentation for Domain Generalization},
author={Shanshan Wang and ALuSi and Xun Yang and Ke Xu and Huibin Tan and Xingyi Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=tVVE2Nq9uj}
}
Domain generalization (DG) task aims to learn a robust model from source domains that could handle the out-of-distribution (OOD) issue. In order to improve the generalization ability of the model in unseen domains, increasing the diversity of training samples is an effective solution. However, existing augmentation approaches always have some limitations. On the one hand, the augmentation manner in most DG methods is not enough as the model may not see the perturbed features in approximate the worst case due to the randomness, thus the transferability in features could not be fully explored. On the other hand, the causality in discriminative features is not involved in these methods, which is harm for the generalization of model due to the spurious correlations. To address these issues, we propose a Dual-stream Feature Augmentation (DFA) method by constructing some hard features from two perspectives. Firstly, to improve the transferability, we construct some targeted features with domain related augmentation manner. Through the guidance of uncertainty, some hard cross-domain fictitious features are generated to simulate domain shift. Secondly, to take the causality into consideration, the spurious correlated non-causal information is disentangled by an adversarial mask, then the more discriminative features can be extracted through these hard causal related information. Different from previous fixed synthesizing strategy, the two augmentations are integrated into a unified learnable model with disentangled feature strategy. Based on these hard features, contrastive learning is employed to keep the semantics consistent and improve the robustness of the model. Extensive experiments on several datasets demonstrated that our approach could achieve state-of-the-art performance for domain generalization.
Dual-stream Feature Augmentation for Domain Generalization
Shanshan Wang ALuSi Xun Yang Ke Xu Huibin Tan Xingyi Zhang