Harald Lykke

Well received PhD defense at the Visual Intelligence Research Centre

Daniel Johansen Trosten defended his PhD thesis “Improving Representation Learning for Deep Clustering and Few-shot Learning” on August 23rd 2023 at UiT The Arctic University of Norway.

Well received PhD defense at the Visual Intelligence Research Centre

Daniel Johansen Trosten defended his PhD thesis “Improving Representation Learning for Deep Clustering and Few-shot Learning” on August 23rd 2023 at UiT The Arctic University of Norway.

Summary of thesis:

The amounts of data in the world have increased dramatically in recent years, and it is quickly becoming infeasible for humans to label all these data. It is therefore crucial that modern machine learning systems can operate with few or no labels.The introduction of deep learning and deep neural networks has led to impressive advancements in several areas of machine learning. These advancements are largely due to the unprecedented ability of deep neural networks to learn powerful representations from a wide range of complex input signals. This ability is especially important when labeled data is limited, as the absence of a strong supervisory signal forces models to rely more on intrinsic properties of the data and its representations.

This thesis focuses on two key concepts in deep learning with few or no labels. First, we aim to improve representation quality in deep clustering - both for single-view and multi-view data. Current models for deep clustering face challenges related to properly representing semantic similarities, which is crucial for the models to discover meaningful clusterings. This is especially challenging with multi-view data, since the information required for successful clustering might be scattered across many views. Second, we focus on few-shot learning, and how geometrical properties of representations influence few-shot classification performance. We find that a large number of recent methods for few-shot learning embed representations on the hypersphere. Hence, we seek to understand what makes the hypersphere a particularly suitable embedding space for few-shot learning.

Illustration of how embeddings are embedded on the hypersphere

Evaluation Committee:

- Professor Klaus-Robert Müller, Technical University of Berlin, Germany (1. Opponent)
- Professor Anne H Schistad Solberg, Institute for Informatics, University of Oslo, Norway (2. Opponent)
- Professor Fred Godtliebsen, Department of Mathematics and Statistics (internal member and leader of the committee)


- Associate Professor Michael Kampffmeyer, Department of Physics and Technology, UiT (main supervisor)
- Professor Robert Jenssen  Department of Physics and Technology, UiT (co-supervisor)
- Researcher Sigurd Løkse, Department of Physics and Technology, UiT(co-supervisor)

The Defense was led by Professor John Sigurd Mjøen Svendsen, Pro-Dean at the Faculty of Science and Technology at UiT.

Daniel Trosten (center) with committee and advisors (Photo: Halald Lykke)

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