Research map / Label-efficient and robust learning / Domain adaptation and generalization
DomCLP: Domain-wise Contrastive Learning with Prototype Mixup for Unsupervised Domain Generalization
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Proposes DomCLP, a domain-wise contrastive learning method with prototype mixup for unsupervised domain generalization (per the abstract; the provided full text is only the AAAI author template).
Problem
Self-supervised instance-discrimination models (InfoNCE) generalize poorly to unseen-domain data; unsupervised domain generalization (UDG) seeks domain-irrelevant SSL representations.
Approach
Uses domain-wise contrastive learning together with prototype mixup to learn domain-irrelevant features under unsupervised domain generalization. Details beyond the abstract are not available in the provided text.
Experimental setup
Builds on: InfoNCE instance discrimination
Closest papers by meaning
- Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization (ICLR 2024 · similarity 0.90)
- Towards Generalizing to Unseen Domains with Few Labels (CVPR 2024 · similarity 0.87)
- Unknown Domain Inconsistency Minimization for Domain Generalization (ICLR 2024 · similarity 0.87)
- Mixup-Induced Domain Extrapolation for Domain Generalization (AAAI 2024 · similarity 0.87)
- Minimal Semantic Sufficiency Meets Unsupervised Domain Generalization (NeurIPS 2025 · similarity 0.87)
- Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization (CVPR 2025 · similarity 0.87)
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