Research map / Label-efficient and robust learning / Domain adaptation and generalization
Cross-Domain Visual Representations via Unsupervised Graph Alignment
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| align distribution structure via graph edge criteria and consistency loss in addition to adversarial node alignment (vs ADDA) | adversarial domain adaptation, LeNet · Digit recognition average | accuracy (%): 71.0 -> 77.4 | Helped |
| graph alignment vs ADDA on noisy SVHN source | adversarial domain adaptation, LeNet · Digit recognition M->S | accuracy (%): 34.4 -> 32.0 | Hurt |
| graph alignment vs ADDA on S->M | adversarial domain adaptation, LeNet · Digit recognition S->M | accuracy (%): 63.2 -> 85.0 | Helped |
| graph alignment vs ADDA with LeNet backbone | LeNet · Office-10+Caltech-10 average | accuracy (%): 66.0 -> 82.9 | Helped |
| graph alignment vs DAN with AlexNet backbone | AlexNet · Office-10+Caltech-10 average | accuracy (%): 87.3 -> 88.6 | Helped |
| graph alignment vs plain AlexNet features | AlexNet · RegDB Visible->Thermal test | mAP (%): 7.9 -> 10.7 | Helped |
| graph alignment vs plain AlexNet features | AlexNet · RegDB Thermal->Visible test | mAP (%): 7.8 -> 13.0 | Helped |
Closest papers by meaning
- Joint Geometrical and Statistical Alignment for Visual Domain Adaptation (CVPR 2017 · similarity 0.89)
- Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment (AAAI 2025 · similarity 0.89)
- GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation (CVPR 2019 · similarity 0.88)
- Graph-Relational Domain Adaptation (ICLR 2022 · similarity 0.88)
- Rethinking Propagation for Unsupervised Graph Domain Adaptation (AAAI 2024 · similarity 0.86)
- Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation (NeurIPS 2024 · similarity 0.86)
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