Papers with OOD instances
Contrastive Out-of-Distribution Detection for Pretrained Transformers (2021.emnlp-main)
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| Challenge: | Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution, but in real-world scenarios, out-of-distribution instances can cause semantic shift problems. |
| Approach: | They propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, and to use the Mahalanobis distance in the model's penultimate layer to detect OOD instances. |
| Outcome: | The proposed method outperforms baselines in the real-world and achieves near-perfect OOD detection performance. |