Research map / Label-efficient and robust learning / Domain adaptation and generalization
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use 2ST-UDA instead of the strongest listed baseline, MDAN(S-MAX) | Amazon review sentiment classification · Amazon review dataset | accuracy: +1.42 | Helped |
| Use WS-UDA instead of the strongest listed baseline, MDAN(S-MAX) | Amazon review sentiment classification · Amazon review dataset | accuracy: +0.03 | Helped |
| Use WS-UDA instead of the strongest listed baseline, Meta-MTL | FDU-MTL sentiment classification · FDU-MTL | accuracy: +1.2 | Helped |
| Use 2ST-UDA instead of the strongest listed baseline, Meta-MTL | FDU-MTL sentiment classification · FDU-MTL | accuracy: +1.5 | Helped |
Closest papers by meaning
- Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning (AAAI 2020 · similarity 0.89)
- Multi-Source Domain Adaptation for Visual Sentiment Classification (AAAI 2020 · similarity 0.89)
- Adversarial Multiple Source Domain Adaptation (NeurIPS 2018 · similarity 0.89)
- Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation (ICLR 2026 · similarity 0.88)
- Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits (AAAI 2020 · similarity 0.88)
- Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? (ICLR 2024 · similarity 0.87)
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