Difference between revisions of "Out-of-Distribution (OOD) Generalization"
m |
m |
||
| Line 24: | Line 24: | ||
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. | 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. | ||
| − | |||
<youtube>W3XE9yD5H4A</youtube> | <youtube>W3XE9yD5H4A</youtube> | ||
| Line 34: | Line 33: | ||
<youtube>v27iTSkYugU</youtube> | <youtube>v27iTSkYugU</youtube> | ||
<youtube>_ZpBgkpgPp8</youtube> | <youtube>_ZpBgkpgPp8</youtube> | ||
| + | |||
| + | = Difference Between In-Context Learning and OOD Generalization = | ||
| + | * [[In-Context Learning (ICL)]] ... [[Context]] | ||
| + | |||
| + | [[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 (ICL)|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. | ||
Revision as of 17:23, 27 May 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- In-Context Learning (ICL) ... Context ... Out-of-Distribution (OOD) Generalization
- Singularity ... Sentience ... AGI ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Mathematical Reasoning
- Transfer Learning
- Causation vs. Correlation
- Towards Out-Of-Distribution Generalization: A Survey
- Towards a Theoretical Framework of Out-of-Distribution Generalization
- Out-of-Distribution Generalization via Risk Extrapolation
- Using Interventions to Improve Out-of-Distribution Generalization of ....
- 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.
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.
