Out-of-Distribution (OOD) Generalization
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- Towards Out-Of-Distribution Generalization: A Survey
- Towards a Theoretical Framework of Out-of-Distribution Generalization
- Out-of-Distribution Generalization via Risk Extrapolation
- [https://arxiv.org/abs/2210.10636 Using Interventions to Improve Out-of-Distribution Generalization of ....
- [https://link.springer.com/chapter/10.1007/978-3-030-92659-5_39 How Reliable Are Out-of-Distribution Generalization Methods for Medical ....
- Meta-Causal Feature Learning for Out-of-Distribution Generalization ...
Out-of-Distribution (OOD) generalization refers to the ability of a machine learning model to generalize to new data that comes from a different distribution than the training data. This is a challenging problem because the testing distribution is unknown and different from the training distribution. There are several methods for improving out-of-distribution generalization. According to a survey on the topic, existing methods can be categorized into three parts based on their positions in the whole learning pipeline: unsupervised representation learning, supervised model learning and optimization. Another approach to out-of-distribution generalization is via learning domain-invariant features or hypothesis-invariant features.
