Multi-View Representation Learning in Computer Vision, Deep Learning, Computer Vision, Multi-View Data – PhD (Funded) Ref: 3756
About the award
Dr Anjan Dutta, University of Exeter
Department of Computer Science, Streatham Campus, Devon, Exeter
The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully funded PhD studentship to commence in March 2020 or as soon as possible thereafter. For eligible students the studentship will cover UK/EU/International tuition fees plus an annual tax-free stipend of at least £15,009 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Innovation Centre Phase 1 in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.
Multi-view representation learning is concerned with the problem of learning representations (or features) of the multi-view (multi-modal) data that facilitate extracting readily useful information when developing prediction models. This learning mechanism has attracted much attention since multi-view data have become increasingly available in real world applications where examples are described by multi-modal measurements of an underlying signal, such as text + image, sketch + image, audio + video, and many others. Generally, data from different views usually contain complementary information, and multi-view representation learning exploits this point to learn more comprehensive and robust representations than those of single-view learning methods. Since the performance of machine learning methods is heavily dependent on the expressive power of data representation, multi-view representation learning has become a very promising topic with wide applicability. Following the success of deep neural networks, several deep multi-view learning methods are recently proposed based on deep learning. These methods usually apply multi-view learning criteria on top of multiple single-view deep networks; generally, a two-stage scheme of iterative learning is usually adopted to train the network parameters, where view specific features are learned until the very top layers. This is usually done by following a sequential step of multi-view criteria followed by the objectives of the specified learning tasks or regularized learning objectives that seek a balance between multi-view criteria and the final tasks of interest. In this project, our objective is to explore and develop different deep learning models that can solve various computer vision problems involving multi-view data, such as sketch and text-based image retrieval, unsupervised image clustering and classification etc.
This award provides annual funding to cover UK/EU/International tuition fees and a tax-free stipend of at least £15,009 per year.
The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence in March 2020.
Some of the necessary skills that are needed for the project are as follows:
- Mathematics: Linear algebra, probabilities
- Computer Science: Programming skills in Python, ideally already some experience with PyTorch or TensorFlow.
- Other: Working knowledge of LaTeX
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.
If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project. Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).
How to apply
In the application process you will be asked to upload several documents.
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Research proposal
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to firstname.lastname@example.org quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.
The closing date for applications is midnight on 20th December 2019. Interviews will be held on the University of Exeter Streatham Campus the week commencing 6th January 2020.
If you have any general enquiries about the application process please email email@example.com or phone +44 (0)1392 722730 or +44 (0)1392 725150. Project-specific queries should be directed to the main supervisor.
|Application deadline:||20th December 2019|
|Value:||£15,009 per year for 3.5 years|
|Duration of award:||per year|
|Contact: PGR Admissions Office +44 (0)1392 722730 / firstname.lastname@example.org|