Out-of-Distribution (OOD) Generalization
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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.
Difference Between In-Context Learning and OOD Generalization
In-Context Learning (ICL) refers to the ability of a machine learning model to learn from a few examples provided in the context of a task, without any fine-tuning. This is also known as few-shot learning or zero-shot learning.
Out-of-distribution (OOD) generalization, on the other hand, refers to the ability of a machine learning model to generalize to new data that comes from a different distribution than the training data1.
The main difference between In-Context Learning (ICL) and OOD generalization is that in-context learning focuses on the ability of a model to learn from a few examples provided in the context of a task, while OOD generalization focuses on the ability of a model to generalize to new data that comes from a different distribution than the training data.
