Self-Supervised Training for Industrial Applications: A Master Thesis Opportunity
4 days ago
The proposed master thesis project focuses on investigating the potential of self-supervised pre-training for industrial deep learning applications. The goal is to explore whether self-supervised learning can overcome the performance of traditional pre-training methods on open-source datasets.
Research Questions- Which self-supervision method performs best in an industrial context?
- Which self-supervision method is suitable for convolutional neural networks versus transformers?
- How many domain images without annotations are needed?
- How many annotated application images are needed?
- Is it required to use a large base dataset to reach optimum performance?
A solid foundation in image processing and deep learning is beneficial for assessing and collecting relevant methods. Experience in self-supervision or training of deep neural networks is also welcomed. Some experience in Python programming is required.
About SICK IVPSICK IVP is a world-leading supplier of sensors and sensor solutions for industrial applications. Our team in Linköping is an innovation center for Machine Vision, committed to developing and delivering software for technically leading products within the field of 2D and 3D vision, as well as system solutions for robot guidance and quality control.
We are proud of being a healthy and attractive workplace, recognized as one of the best workplaces in Sweden. We work actively to reduce our climate footprint and contribute to the society and increase diversity at our workplace.
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