Out-of-Distribution (OOD) Generalization
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- Towards Out-Of-Distribution Generalization: A Survey | Z. Shen, J. Liu, Y. He, X. Zhang, R. Xu, H. Yu, P. Cui - arXiv - Cornell University
- Towards a Theoretical Framework of Out-of-Distribution Generalization | H. Ye, C. Xie, T. Cai, R. Li, Z. Li, L. Wang - arXiv - Cornell University
- Out-of-Distribution Generalization via Risk Extrapolation | D. Krueger, E. Caballero, J. Jacobsen, A. Zhang, Jonathan Binas, D. Zhang, R. Le Priol, A. Courville
- Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems | P. Bansal, Y. Prabhu, E. Kiciman, A. Sharma
- How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation? | A. Sanner, C. González & A. Mukhopadhyay
- Meta-Causal Feature Learning for Out-of-Distribution Generalization | Y. Wang, X. Li, Z. Qi, J. Li, X. Li, X. Meng & L. Meng
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.
Difference Between Transfer Learning and OOD Generalization
Transfer Learning is a machine learning method that reuses a trained model designed for a particular task to accomplish a different yet related task. The knowledge acquired from task one is thereby transferred to the second model that focuses on the new task.
Out-of-Distribution (OOD) generalization is a problem in machine learning that addresses the challenging setting where the testing distribution is unknown and different from the training1. This problem is also known as domain generalization.
In summary, Transfer Learning deals with reusing knowledge from one task to improve performance on another related task while OOD generalization deals with the problem of generalizing to unknown and different distributions.
Difference Between Autocorrelation and OOD Generalization
Autocorrelation is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.
On the other hand, Out-of-Distribution (OOD) generalization is a problem in machine learning that addresses the challenging setting where the testing distribution is unknown and different from the training.
In summary, autocorrelation deals with finding repeating patterns in signals while OOD generalization deals with generalizing to unknown and different distributions.
