A Brief Review of Domain Adaptation

By Abolfazl Farahani, Sahar Voghoei, Khaled M. Rasheed, Hamid Reza Arabnia. Advances in Data Science and Information Engineering, 2020

810 citations recorded by the source corpus.

Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, This assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e.g., collecting the training and test sets from different sources, or having an out-dated training set due to the change of data over time. In this case, there would be a discrepancy across domain distributions, and naively applying the trained model on the new dataset may cause degradation in the performance. Domain adaptation is a sub-field within machine learning that aims to cope with these types of problems by aligning the disparity between domains such that the trained model can be generalized into the domain of interest. This paper focuses on unsupervised domain adaptation, where the labels are only available in the source domain. It addresses the categorization of domain adaptation from different viewpoints. Besides, It presents some successful shallow and deep domain adaptation approaches that aim to deal with domain adaptation problems.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/115bbc50cf015990dc6f35bc8f99575ad2a68253

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Citations in the corpus

A Review of Deep Transfer Learning and Recent Advancements (2022, background): “Before the rise of deep learning models, transfer learning was known as domain adaptation and focused on homogenous data sets and how to relate such sets to each other because of the nature of ML algorithms [2][3].”
Data-centric Artificial Intelligence: A Survey (2023, positive): “Biased data sampling can be used to synthesize covariate shifts or label shifts, whereas learning-based methods are typically required to synthesize general distribution shifts [68, 86].”
Data Feedback Loops: Model-driven Amplification of Dataset Biases (2022, background): “Data feedback has connections to various domain adaptation settings (Farahani et al., 2021; Shu et al., 2018; Kumar et al., 2020; Lipton et al., 2018), where the changing data distributions can be viewed as shifting target domains.”