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Essential Reading: Top 10 Surveys in Transfer Learning and Domain Adaptation for Computer Vision

Dive into the Evolution and Impact of Transfer Learning and Domain Adaptation through these Influential Surveys

10 surveys about transfer learning and domain adaptation in the domain of computer vision you need to read:

  1. A Survey on Transfer Learning (2010)

The survey categorizes and reviews transfer learning's progress for classification, regression, and clustering, discussing its relationship with domain adaptation, multitask learning, and co-variate shift. โ†’ read here

  1. Transfer learning for activity recognition: a survey (2013)

It characterizes approaches by sensor modality, environment differences, data availability, and information type transferred. โ†’ read here

  1. Visual Domain Adaptation A survey of recent advances (2015)

This survey reviews visual recognition domain adaptation methods, evaluates their strengths and limitations, and identifies promising research areas. โ†’ read here

  1. Transfer learning using computational intelligence: A survey (2015)

It systematizes computational intelligence-based transfer learning techniques into categories like neural networks and Bayes-based methods and discusses their applications. โ†’ read here

  1. Transfer Learning for Visual Categorization: A Survey (2015)

It surveys algorithms in object recognition, image classification, and human action recognition, highlighting transfer learning's role in leveraging cross-domain data. โ†’ read here

  1. Deep visual domain adaptation: A survey (2015)

It introduces a taxonomy of adaptation scenarios, summarizes approaches by training loss, and reviews applications beyond image classification. โ†’ read here

  1. A survey of transfer learning (2016)

Defines transfer learning, reviewing solutions and applications in contexts where training and testing data domains differ. It discusses transfer learning's applicability in big data environments. โ†’ read here

  1. A survey of transfer learning for collaborative recommendation with auxiliary data

The survey discusses the role of Intelligent Recommendation Technology in industries like e-commerce, focusing on Collaborative Recommendation with Auxiliary Data. โ†’ read here

  1. Extreme learning machine based transfer learning algorithms: A survey (2017)

It provides a comprehensive overview of ELM-based transfer learning, serving as a guide for new researchers and identifying future research avenues. โ†’ read here

  1. Domain adaptation for visual applications: A comprehensive survey (2017)

It discusses shallow and deep domain adaptation methods, and their effects on various visual tasks, and relates domain adaptation to other machine learning solutions. โ†’ read here

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