Master Thesis: Semi-supervised learning with extremely imbalanced data
Do you want to create technology for the next generation of industry and society? Univrses is now looking for a Master Thesis student to join the team in Stockholm!
Univrses is a 3D Computer Vision and Machine Learning company based in Stockholm, creating high-end technologies for autonomous systems. We work in several different areas, but our main focus is on self-driving vehicles, mobile robotics and smart city development. Our team consists of hard-working and friendly people from all over the world with diverse backgrounds and expert knowledge in computer vision, robotics, machine learning, physics, math, software development and more.
Master Thesis at Univrses
Right now, we offer Master students the opportunity to be an integral part of the team while working on their Thesis. You will be working in a fun, stimulating and highly professional work environment together with our awesome team. This is truly a unique chance to work with some of the best scientists and engineers in Computer Vision and Robotics in the world.
Background
Semi-supervised learning is a machine learning paradigm that leverages both labelled and unlabeled data to improve model performance. In many real-world scenarios, however, datasets are characterized by extreme class imbalance, with very few labelled examples for certain classes. This imbalance presents a significant challenge for traditional semi-supervised learning methods, as they tend to focus on the majority class, neglecting the underrepresented classes. This thesis aims to explore novel techniques and strategies for improving semi-supervised learning performance in the presence of extreme class imbalance.
Goals
The primary objectives of this research are as follows:
- Method Development: Develop and evaluate novel semi-supervised learning techniques specifically designed to handle datasets with extreme class imbalance. These methods should effectively leverage the small amount of labelled data available for minority classes while making use of abundant unlabeled data.
- Performance Improvement: Evaluate the extent to which the model trained with the novel semi-supervised learning technique outperforms models trained only on labelled data.
- Real-world Applications: Apply the proposed methods to real-world products, where class imbalance is a common challenge. Assess the practical utility and effectiveness of the developed techniques in these domains.
Relevant material
- Class-Imbalanced Semi-Supervised Learning
- Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
-
A Survey on Semi-Supervised Semantic Segmentation
Required Background
Applicants are expected to possess a comprehensive understanding of deep learning and computer vision, which will be essential for engaging with the research effectively.
Proficiency in Python coding is a prerequisite for implementing and experimenting with the proposed techniques.
Required application material
- CV
- Cover letter
- Transcripts (with grades)
All application material must be in English.
Work starts in beginning of January 2024
Next step
Interested in joining Univrses as our new Master Thesis student? Submit your application - All application material must be in English!
- Department
- Tech
- Role
- Master Thesis Student
- Locations
- Stockholm
- Remote status
- Temporarily Remote
Stockholm
Working at Univrses
-
World-leading technologies
You will work with unique solutions that will change the way industries operate and societies develop -
World-leading team
You will work in a diverse and friendly team with the world's most skilled scientists and expert engineers within the field -
Generous employee benefits
You will be offered a competitive salary, flexible work hours, pension plan and weekly Lunch&Learns, among other things -
Offices in the center of Stockholm
During non-pandemic times, you will work from our bright and spacious facilities in the heart of Södermalm, Stockholm
About Univrses
Univrses is a 3D Computer Vision and Machine Learning company, based in Sweden. We create high-end technologies for autonomous systems, and specialize in self-driving vehicles, mobile robotics and smart city development. The team consists of programmers and PhDs within fields such as computer vision, machine learning, robotics, software development, maths and physics.
Master Thesis: Semi-supervised learning with extremely imbalanced data
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