Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
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| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
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