Laviza Falak Naz1, Rohail Qamar2,*, Raheela Asif1, Muhammad Imran2, Saad Ahmed3
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 855-887, 2024, DOI:10.32604/iasc.2024.055074
- 31 October 2024
Abstract Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions. Domain shift will reduce accuracy in results. To prevent this, domain adaptation is done, which adapts the pre-trained model to the target domain. In real scenarios, the availability of labels for target data is rare thus resulting in unsupervised domain adaptation. Herein, we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks (GANs) are integrated to improve the performance of computer vision or robotic vision-based systems in… More >
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