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Collaborative image annotation for Deep Learning

Reducing the need for large initial training data sets by including a human domain expert in the loop when training classification models from annotated data.

(Photo: Pixabay)

Description

Problem description

Availability of annotated (labelled) images is crucial to train a Deep Learning (DL) model that can classify and predict objects and events under water correctly. Existing approaches to developing a DL classifier model often require large amounts of training data which is a resource consuming task. Recently, technical approaches have been proposed to address and reduce the need for large initial training data sets by interactively incorporating a human domain expert in the loop when training classification models from annotated data. These approaches allow a human expert to manually correct or revise predictions with low confidence scores, and the initial model is then trained with the revised data.

Expected Results and Learning Outcome

  • Approach for human-in-the-loop annotation of images in a subsea context
  • Software prototype
  • Evaluation of prototype with real business use cases

Research Topic

This MSc thesis aims at developing a human-in-the-loop approach for collaborative annotation of subsea images.

Recommended prerequisites

Basic knowledge about Machine Learning and Deep Learning approaches will be considered an advantage.

Methods

  • Identify Deep Learning and Cooperative Machine Learning approaches and algorithms for semi-automatic labelling of objects and events.
  • Develop an approach that will address and reduce the need for large initial training data sets by interactively incorporating a human domain expert in the loop when training classification models from annotated data.
  • Implement the approach in a software prototype.
  • Evaluate the approach by using a relevant use case pilot in a current SINTEF innovation project.

Supervisor

Main supervisor: Maryna Waszak  -  Assisting supervisor: Brian Elvesæter

Contact info