Out-of-Distribution Generalization

Out-of-Distribution Generalization (OOD generalization) aims to solve the problem of model generalization ability when the distribution of training data is inconsistent with the distribution of test data. It focuses on how to enable the model to maintain good performance and stability when facing unknown or unseen data distribution.

In traditional machine learning tasks, it is usually assumed that the training data and test data come from the same distribution (independent and identically distributed, iid). However, in real-world applications, this assumption often does not hold. For example, the training data may come from a specific environment or condition, while the test data may come from a completely different environment. This difference in distribution can cause the performance of the model on the test data to drop significantly. The goal of out-of-distribution generalization is to solve this distribution shift problem so that the model can adapt and generalize to unseen data distributions.