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What is Domain Adaptation?

Domain adaptation is a sub-discipline of machine learning which deals with scenarios in which a model trained on a source distribution is used in the context of a different (but related) target distribution.
What is Domain Adaptation?

In general, domain adaptation uses labeled data in one or more source domains to solve new tasks in a target domain. The level of relatedness between the source and target domains hereby usually determines how successful the adaptation will be.

There are multiple approaches to domain adaptation. In “shallow” (not deep) domain adaptation, two methods are commonly used: reweighing the source samples and training on the reweighed samples, and trying to learn a shared space to match the distributions of the source and target datasets. While these techniques may also be applicable in the context of deep learning, the deep features learned by Deep Neural Networks (DNNs) usually give rise to more transferable representations (generally learning highly transferable features in the lower layers while the transferability sharply decreases in higher layers,. In Deep Domain Adaptation, we try to make use of this property of DNNs.

Deep Domain Adaptation In Computer Vision
Adapting generic source models in computer vision to domain-specific target tasks
https://towardsdatascience.com/deep-domain-adaptation-in-computer-vision-8da398d3167f

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