Study level

PhD

Faculty/School

Science and Engineering Faculty

School of Electrical Engineering and Robotics

Topic status

We're looking for students to study this topic.

Supervisors

Dr Feras Dayoub
Position
Senior Lecturer
Division / Faculty
Science and Engineering Faculty

Overview

This project breaks the current 'weak' assumption in the literature that the performance of deep learning models reported on a holdout dataset is an indicator of the performance on all future and yet to be encountered conditions during deployment. In reality, performance fluctuates and can drop below critical thresholds when the robot travels through particular places, times and conditions.

Research activities

This PhD project will investigate new methods and approaches to performance monitoring of deep learning-based perception without the need for ground truth data including uncertainty estimation, dataset shift and domain shift detection, and change detection.

Outcomes

We expect the outcomes of this project to include the development of new algorithms that can monitor the performance of deep learning-based perception modules in real-time, by extracting cues from extrospective and introspective data during robotic deployment.

Skills and experience

To successfully complete this project, you will need to have:

  • strong programming skills in a language such as Python
  • good general knowledge of deep learning and computer vision
  • a strong interest in robotics application and enabling robots to work in the real world.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.