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Found 5 papers, 5 papers with code
SETA: Semantic-Aware Token Augmentation for Domain Generalization
In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information.
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
2) From the feature perspective, the simple Tail Interaction module implicitly enhances potential correlations among all samples from all source domains, facilitating the acquisition of domain-invariant representations across multiple domains for the model.
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Deep Neural Networks have exhibited considerable success in various visual tasks.
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
However, the local operation of the convolution kernel makes the model focus too much on local representations (e. g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability.
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Particularly, the proposed method can generate a variety of data variants to better deal with the overfitting issue.
