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About the Project
Transfer learning has been demonstrated to improve the generalisation of deep neural networks to real-world applications, especially in situations where labelled training sets are missing. Current proposed methods work under certain conditions or tasks and, although a few universal approaches have been proposed, there are still several gaps, such as preventing negative transfer or catastrophic forgetting.
The successful applicant will conduct research on transfer learning approaches, using partially labelled, or even unlabelled, datasets. The development of novel transfer learning approaches will be evaluated on real-world inter-disciplinary computer vision problems, such as plant and medical image analysis.
Prospective applicants are encouraged to contact the Supervisor before submitting their applications. Applications should make it clear the project you are applying for and the name of the supervisor(s).
Academic qualifications
A first degree (at least a 2.1) ideally in computer science (with an AI specialisation) with a good fundamental knowledge of machine learning.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Essential attributes:
- Experience of fundamental machine learning and deep learning
- Competent in python programming
- Knowledge of transfer learning and/or domain adaptation
- Good written and oral communication skills
- Strong motivation, with evidence of independent research skills relevant to the project
- Good time management
Desirable attributes:
- Knowledge of (or experience with) transfer learning approaches
- Experience with adversarial learning
- Working Knowledge of probability and statistics
For enquiries about the content of the project, please email Dr Valerio Giuffrida v.giuffrida@napier.ac.uk
For information about how to apply, please visit our website https://www.napier.ac.uk/research-and-innovation/research-degrees/how-to-apply
To apply, please select the link for the PhD Computing FT application form
References
Litrico, Mattia, Sebastiano Battiato, Sotirios A. Tsaftaris, and Mario V. Giuffrida 2021. “Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap” Journal of Imaging 7, no. 10: 198. https://doi.org/10.3390/jimaging7100198

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